DuckDuckGo is a free browser you download to your mobile and desktop devices. Unlike Chrome, the DuckDuckGo browser has privacy built in, including our private alternative to Google search that doesn't spy on your searches, our best-in-class tracker blocker that blocks cookies and creepy ads that follow you around, and more. We've been an independent company since our founding in 2008. Our mission is to make getting privacy simple and accessible for everyone. Join tens of millions of people who rely on DuckDuckGo’s free all-in-one privacy solution to help take control of their online privacy. Install the DuckDuckGo browser on your mobile and desktop devices, today!

DuckDuckGo

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An Analyst suggested that should Apple buy DuckDuckGo, a privacy-focused search engine. Although, Apple most likely won’t buy DuckDuckGo, it might still be interested in creating a search engine. About a week earlier analyst Toni Sacconaghi put forward a recommendation for Apple. He believes Apple can buy DuckDuckGo, a privacy centred search engine and make it the default search engine in all Apple devices. Thus finally ending its business with Google, which currently pays $7-8B to Apple to remain the same. That analyst says so because both Apple and are super privacy-focused companies. DuckDuckGo Note: DuckDuckGo is hereafter referred to as DDG because it’s too long to type. You might not know about DuckDuckGo. And that’s fairly normal. Because DDG receives about 62 million queries a day. That’s huge but not even close to Google’s numbers (3.5 billion). Number of daily searches on four top search engines. What is DuckDuckGo? A search engine that began in a basement in 2008. Their aim was to serve a better search engine and in 2010 they set their unique selling point: they won’t track users. Because Google does. Since then the company has grown immensely. Currently, they rank #4 in the list of top search engines. They also donate yearly to internet freedom foundations like the Tor Project, and SecureDrop. DuckDuckGo advertisings. // Source: Wired I won’t say DDG is as good as Google. Mainly because it has just 93 employees. But when you change the scale to privacy, there’s no better option than DDG. Even the Tor browser, which is a really privacy-focused browser has DDG as it’s a default search engine. So there are areas where DDG isn’t as good and there are also areas where there’s no better option. Coming back to the story, people have kinda mixed opinion on whether or not Apple should buy DDG. Let’s get to them one by one. This will undermine a major aspect of DDG While some say DuckDuckGo can be a good addition to Apple’s privacy promise, saying, “One of DuckDuckGo’s biggest and most important aspect is that no one big is pulling the strings. This would certainly undermine that.” some disagree Well, that’s true. There’s no big company that funds DDG. And since they’re just 93 employees, they cover up all costs. They don’t require outside funding. They don’t need to think twice before tweaking the search algorithm for better privacy services even if any big tech company doesn’t like that. But if Apple buys it, Apple will obviously prioritise its business first. This isn’t the best time to make enemies with Google Apple doesn’t seem to be in any mood to take up a rivalry with Google especially in these times. Because the two tech giants have been absurdly friendly. The joined hands in launching the contact tracing system. And Google CEO Sundar Pichai said he’d be interested in working in future projects with Apple. Now in these times, Google won’t take it nicely if Apple heats up the competition in their space. Google’s slowly getting into Apple’s business. It might be time to retreat. See, Apple mostly deals with phones, computers, smartwatches, smart speakers, wireless headphones to name a few. And previously, Google has also entered into these spaces. They have their Pixels series competing with the iPhones, the Chromebooks competing with MacBooks, the Google Watch OS competing with Apple watch. Just to name a few. So it might be Apple’s turn to invade Google. I just hope that goes well. Because Google currently has an unbeatable dominance in the space. But conversely, this is also a reason why Apple might consider heading into the search business. Apple’s public image will improve Apple does a great job making people believe that Apple products mean privacy guaranteed. Apple has undoubtedly been successful in forming a great public image. With billboards and signs all over, CEO Tim Cook proudly says “We at Apple believe that privacy is a fundamental human right.” “My investigation suggests otherwise.” Geoffrey A. Fowler, The Washington Post Everyone except experts believes it’s true while it isn’t. Even when Google pays Apple to be the default search engine, Google does track a lot of data and uses it to display ads. Geoffrey A. Fowler from notes that midnight his iPhone is sending data to several companies he’s never heard of. The Washington Post So experts quite disagree on the fact that Apple is privacy-friendly. But it’s made its public image says the quite opposite. It has some degrees of privacy protections but not full proof system. very Buying DDG will only enhance this image. DDG isn’t Google-level smart DuckDuckGo is partly based on Microsoft’s Bing. And it’s not so smart. I mean using DDG is like compromising on speed and ease of use, to get privacy. And currently, it isn’t as good a tradeoff. DuckDuckGo doesn’t always show the result you want. Even if you specify the site name. While Google does it best. That means many users might still want to switch back to Google and Apple can’t deny them. The most it can do is to hide the options deep in settings. But if that happens, the internet and Google itself will show them how to do it. And Apple’s image will get down too. Now, in case, Apple involves its own team to improve DuckDuckGo, they will surely not call it DDG instead it might be an addition to Siri. If Apple wanted its own Search Engines, It’d have made it from the scratch On a broad scale, there’s nothing much Apple will get even if it buys DDG. DDG can definitely not compete with Google with its bad algorithm (still better than Siri, though.) and Apple will have to make it from scratch. Further, Apple, historically, prefers to make something rather than buying it. So why waste money in DDG? Instead, Apple can make it’s own search engines. A privacy-focused one. Maybe an Ad-free one. But there are still some problems. Because something similar has happened with Google before. And Google knows how to retaliate. How will Google React If This Happens? Let’s take a moment and go back to 2006 when Google used to be the default search engine of Microsoft’s Internet Explorer, one of the biggest sources of traffic for Google then. And then Microsoft dropped a bomb on Google, launching Bing and making it the default search engine of the Internet Explorer. Startled, Google tried to find ways to get out of this situation. And then one Google employee finally came up with a brilliant idea. He convinced CEO Larry Page and launched what he called the Google Toolbar. The Google Toolbar for the Internet Explorer // Source: The Way Back Machine He convinced a lot of manufacturers to pre-install this toolbar on their computers and thus Google regained the traffic it was losing. It was just a temporary solution though. he convinced the CEOs to allow him to lead a project in making a browser. The world’s most used browser today, Google Chrome. This employee is Sundar Pichai, the current CEO of Google and Alphabet. Back to our main story, this means even if Apple makes a new search engine or buys DDG, Google will come up with creative ways to get people back. Maybe a few million bucks in advertising to teach Apple users how they can set Google back as default will help them regain one of their major sources of traffic which earns them over $15B. And not to forget, they won’t also have to pay Apple $8B now. So this sums up to be $23B. And folks at Apple aren’t stupid that they’d let this happen. Just One More Thing… While writing this, another thought struck me and I want to share it. See, Google pays Apple to be its default search engine because Apple has other options. But if Google later changes its mind, Apple will be left with just two options: either let users use a shitty search engine like Bing or let Google be the default without getting paid. If they choose the latter option, Google will stop paying it to be the default option forever. And if they choose the first option, Apple devices users will have a bad experience. Both cases, there’s Apple’s loss. So, possibly, Apple might consider stepping into the search engine game. So if Google refuses to pay Apple, it can immediately use its own engine. And considering that recent services from Apple have been subscription-based, Apple can make this search engine an optional subscription that allows Apple users to pay a fee and use a search engine that doesn’t track you, is ad-free and results are great. The first two benefits are easy, all depends upon the third one. BTW would you like to pay like $5/$10 a month for such a search engine? This article was published as a part of Theciva's Opinion-Driven series . Sign up for the free newsletter.

Should Apple Buy DuckDuckGo? Or Create Its Own Search Engine? 

There’s no denying that we are years away from seeing any other search engine take over Google. Yes, it has grown thaaaaat big and has left us so dependent on itself that it just can’t happen so quick. Google tracks almost all your activities through your smart devices — especially so in the case of Android devices. The places you visit, the steps you walk, the time you spend on each website, the searches you make, the products you click on and so on. If you are using Google devices and/or its products, Google is behind you. Everywhere. All the time. (If you’re wondering, yes, Google knows exactly what you watched the last night and the night before it and throughout the year) Why is this important for you to know? Tell me, why not? It’s your data. You don’t share it with your girlfriend, why’d you share it with anyone else? On the Google search engine part, the impact of tracking data (searches/activity) reflects in the biased search suggestions and results that you see when you search a keyword on Google. So, basically, Google bombards you with information that it “ ” aligns with your prior activities. thinks This is where comes in. It is a search engine designed to treat you like an anonymous user every time you use it. Simply put: it does not track you or your activity. DuckDuckGo This means that everything you search through DuckDuckGo stays between you and the browser only. DuckDukcGo‘s motto? Simplifying privacy in the age of the internet and “setting the new standard of trust online.” DuckDuckGo/About These snapshots speak for themselves If you wish to verify any of this, just follow steps one to three on both the search engines. Open your incognito tab and go to Google.com Search for any keyword. I searched for “ content writer. ” Once the search result comes up, type different words and notice what Google suggests. I typed ‘what is’, ‘how ’, ‘website’. And that was it. Google knew what I might be interested in next. Snapshot Google Snapshot Google Snapshot Google Repeat the same with . And here are the results. DuckDuckGo On the other hand, when you use DuckDuckGo, it shows you a completely unique suggestion that hardly has any correlation with your previous searches. Snapshot DuckDuckGo Snapshot DuckDuckGo Snapshot DuckDuckGo Now, I guess, it’s more clear to you. DuckDuckGo has come a long way Despite being nowhere close to the search giant — Google — DuckDuckGo is growing faster than anticipated. With an average growth of 50% each year, the search engine is putting up a remarkable show on the stage. From its commencement in 2008 to date, DuckDuckGo has scaled from 79,839 average daily searches to a grand 33,964,034 average daily search in the first 20 days of 2019. Here’s what the graph looks like for DDG’s commendable growth: DuckDuckGo Traffic How Close is that to Google? Whether you talk in terms of the number of searches or in total revenue, DuckDuckGo is but a “little something” when compared to Google. Here’s a simple comparison for you: What DuckDuckGo does in a day, Google does it within ~12.5 minutes. Yes, that’s ~33 million searches every 12.5 minutes on Google. That’s and 3.5 billion searches every day 1.2 TRILLLLLLLION searches every year. It can be said that DuckDuckGo is not a competitor of Google, but of course, a worthy rival Also, you shouldn’t forget, it’s almost 10 years younger to Google. With 50% growth each year and people becoming more and more aware of data privacy, things might change for DuckDuckGo in the future. At least I’d expect it to. Using Both Their Features to My Benefit While DuckDuckGo wants Google completely gone, I hold a different opinion. I want both of them to be. *that’s like a true consumer* And it’s simply because of the benefits I reap from both of them. I’d be wrong to say that I’d not been a beneficiary of the filtered search suggestions and result that Google displays, and also of its other eminent features. But the fact that Google displays results based on my location and search history and that there’s no choice of shutting it down sometimes — bugs me off. all the time — This makes for the one and only reason why I’ve switched to DuckDuckGo. Being a content writer, I have to conduct a lot of research on various different topics. And for this, I need to rely on unfiltered results and not just the ones that are most relevant in my location. DuckDuckGo is also quite helpful when searching for potential freelancing clients from across the globe as it doesn’t filter anything w.r.t where I live. Doing the same would be next to impossible if I try it on Google. It’ll keep throwing at me what’s “nearest” (no matter how irrelevant) to me. And that’s a truckload of garbage to deal with. Then there are also times when you just open your search engine to explore new stuff. DDG comes quite handy for that as well. It curates the most trending keywords from across the globe that match the word or phrase you type. In the case of Google, it would be that same old crap you’ve already been through 10s of times. In one sentence, DuckDuckGo is what you’d like to go for when you need . unfiltered, unbiased results There are many instances when Google feels like a blessing. Especially because of its . on-search-page features Suppose you search for “ ”, Google automatically brings up a dictionary on the search page itself. So, you do not have to open and navigate to any other tab. dictionary Snapshot Google And this is just great. Google has also mastered the art of aesthetically organising and presenting information to its users in case it’s needed for the searched keyword. For example, here’s what it showed me when I searched for the keyword “ ”, Cricket Snapshot Google That’s the schedule of the most recent cricket matches. Can it get any more organized? Also, if I want to search for some local information, there’s nothing that can beat Google for that. And well, we know exactly ‘how’, Google. We know it. In conclusion, I’d say that for me, personally, it is actually about choosing wisely between the two search engines according to what I need to know. Let me know in the comments which browser you use and what you like or dislike about it? Mohammad is an established technical, B2B, and B2C writer in the blockchain and cryptocurrency niche. For enquiries, you can visit or email him at contact[at]mohammadmusharraf[dot]com https://www.mohammadmusharraf.com

DuckDuckGo Vs. Google: What You Need to Know

DuckDuckGo’s Unique Features

. [Full discolosure: this post was written in promotion of fourweekmba.] It was September 25th, 2008 Gabriel Weinberg, had time and money to devote to projects he felt passionate about. He had been tinkering with hundreds of projects in the last couple of years, since he had sold his previous company. Until he finally found the project he felt more passionate about. How did he know? He kept working at it. The idea might have not sounded exciting at the time. Gabriel wanted to develop a search engine, that would be focused on three elements (which he thought Google was missing). First, provide a search engine less skewered toward advertising. Second, a search engine able to provide direct answers to users‘ questions (what DuckDuckGo calls “Instant Answers”). Third, and what would prove DuckDuckGo‘s killer application over the years, was the focus over privacy. In short, DuckDuckGo challenged the classic assumption, that search engines, to make money, had to harvest huge amounts of users‘ data. Instead, DuckDuckGo, provided relevant answers and advertising mainly on a context-basis. And it threw the data on the fly. At launch day, on September 2008, there were no international newspapers’ headlines. There wasn’t a single trace of DuckDuckGo’s launch. That didn’t happen because it was not what Gabriel Weinberg was looking for. He just wanted to know whether he was on the right track to building the kind of search engine he had in mind. That day he launched DuckDuckGo on a forum, Hacker News. From there he gathered quick feedback, and in the next three years he iterated on that feedback. DuckDuckGo would gain traction by 2011, when he also raised $3 million from Union Square Ventures. From there it followed the so much dreamed Hockey Stick Growth curve: Since a few years, people are questioning whether DuckDuckGo would keep growing, as privacy might become less of a concern in the future. And yet, what I like to call, privacy as a business , has proved extremely powerful for DuckDuckGo. Privacy becomes a business model because it influences the whole business. model Negative marketing against tech giants works as a positive lever Another note of thise privacy-driven , is about how marketing works via negativa. In short, the privacy-based business model will work to dismantle given assumptions on which current tech giants have been built upon. business models Therefore, it provides an alternative, and as such it gives an option in markets that are mostly dominated by one or a very few players. This type of is inspired by the frustration developed over the years, as former startups turned tech giants (what we can call the scaling dilemma). marketing As DuckDuckGo pointed out “Our vision is to set a new standard of trust online. The Internet shouldn’t feel so creepy and getting the privacy you deserve online should be as simple as closing the blinds.” This sort of liberating vision is quite compelling and works as a basis to lower the switching costs associated with getting used with a new search engine. If you’re really motivated by privacy you will push through the initial friction of getting used to DuckDuckGo. From asymmetric to symmetric To recap, as a result of a web that got mostly monetized in an , in the last decades we saw the following: asymmetric ways Money asymmetry Users get a free product, companies pay to gain visibility through that product. The product is completely subsidized. Data asymmetry To sustain this , product have built-in mechanisms to harvest data, which is used to offer a better service, but it’s also repackaged and sold to companies as attention currency. The asymmetry here is that the data the user gets from the free service is small compared to the ability of the tech company to use and monetize that data. In short, where the user has the data in the form of entertainment (news in the feed), this data the user gets is not scalable, and it has limited value. On the other hand, the users’ data get processed by the tech giant in a scalable way. This triggers , that makes the advantage of the tech much larger compared to that of the user. It’s important to highlight that this asymmetry works at individual level, not necessarily at societal level. model network effects platform Marketing asymmetry As the key stakeholder (the user) and the key customer (the company paying for visibility) are two separate entities. The platform has to hide as much as possible the monetization process from users (take the case of to make them look as organic results). This creates a belief in users that most of what they see is free, and the average user ignores the fact that there is a monetization going on (and perhaps the user doesn’t care as long as the service works well). This sort of asymmetry in the communication, transforms marketing in a set of engineering processes, to hide as much as possible how the company monetizes its core asset, so that the user doesn’t feel like a product. Google’s advertising redesign Key takeaways from privacy-as-a-business-model A privacy-based business model, will have specific features that influence its technology, how it monetizes it and how it communicates it. More precisely: It affects product engineering, as a privacy based has to make sure that it collects the minimum amount of data necessary to make the service work well, while throwing it on the fly. business model It changes the monetization strategy: as also the way the platform makes money, even if hidden, it has to be context-based, not behavioral-based. It lowers switching costs as the sole fact that the product offers privacy as , it makes the user pass through the initial friction to switching up. value proposition It is a different type of marketing what we can call contrarian marketing, as it offers an alternative to the current, dominant players, by dismantling their core assumptions.

Privacy As A Service: The DuckDuckGo Application

If you’re unfamiliar with DuckDuckGo, they are an Internet privacy company that empowers you to seamlessly take control of your personal information online, without any tradeoffs. Whenever you are connected to the internet your data can be collected, tracked, and used against you, Google and Facebook are tracking you, what you watch, where you shop, even when you sleep, their hidden trackers follow you through the internet on millions of sites and apps. They build detailed profiles and allow advertisers to bid on you, that's how their business works. Over the years, DuckDuckGo has offered millions of people a private alternative to Google, serving over . However, they are excited to launch fully revamped versions of our browser extension and mobile app, extending DuckDuckGo's protection beyond the search box to wherever the Internet takes you. 16 billion anonymous searches It’s hard to use the Internet without it feeling a bit creepy – like there’s a nosey neighbor watching everything you do from across the street. Except, instead of a nosey neighbor, it’s a vast array of highly sophisticated tracker networks, run by big companies like Google and Facebook, , often without your knowledge, and selling their findings to the highest bidder via targeted ads. While closing the blinds at home can put a stop to your neighbors, there isn’t something that simple that can work online. Until now. recording everything you do online Today DuckDuckGo is taking a major step to simplify online privacy with the launch of fully revamped versions of DuckDuckGo's browser extension and mobile app, now with built-in tracker network blocking, smarter encryption, and, of course, private search – all designed to operate seamlessly together while you search and browse the web. DuckDuckGo updated app and extension are now available across all major platforms – , , , , and – so that you can easily get all the privacy essentials you need on any device with just one download. Firefox Safari Chrome iOS Android The DuckDuckGo browser extension and mobile app will also now show you a Privacy Grade rating (A-F) when you visit a website. This rating lets you see at a glance how protected you are, dig into the details to see who they caught trying to track you and learn how they enhanced the underlying website's privacy measures. The Privacy Grade is scored automatically based on the prevalence of hidden tracker networks, encryption availability, and website privacy practices. To date, cobbling together an effective privacy solution has required researching complicated technologies, installing multiple add-ons and apps on each device, and often worsening your Internet experience. Others have been unfortunately misled by supposed simple solutions. Think “Incognito” mode blocks Google from watching what you’re doing? Think again. Private browsing modes are marketed to make you think that if it's not in your device’s browser history, it never happened. Sadly, that . couldn't be further from the truth With the new DuckDuckGo browser extension or mobile app, you are now able to seamlessly: Expose and Block Tracker Networks Watching You The vast majority of websites across the Internet , with Google trackers now lurking behind 76% of pages, Facebook’s trackers on 24% of pages, and countless others soaking up your personal information to follow you with ads around the Web, or worse. DuckDuckGo Privacy Protection will block all the hidden trackers they can find, exposing the major advertising networks tracking you over time, so that you can track who's trying to track you. contain hidden tracker networks Increase Encryption Protection While not all sites offer an encrypted version, thankfully . If DuckDuckGo discovers a site that offers an encrypted version but does not send you to it automatically, DuckDuckGo will. This encryption protects you from eavesdroppers, like ISPs, grabbing up your personal information as it travels across the Internet between you and the websites you visit. this has been rapidly changing Decode a Website’s Privacy Policy Most privacy policies are difficult to understand and aren’t reviewed closely enough. Even if DuckDuckGo block all the tracker networks DuckDuckGo finds, and even if DuckDuckGo upgrade encryption, a website could still be using your data for nefarious purposes, such as selling it to third parties. DuckDuckGo partnered with (TOSDR) to include their scores of website terms of service and privacy policies, where available. However, because most privacy policies still remain unstudied, DuckDuckGo is working with TOSDR to help them to rate and label as many websites as possible. Terms of Service Didn't Read Search Privately Of course, the DuckDuckGo app and extension also include DuckDuckGo private search! You share your most personal information with your search engine, like your financial, medical, and political questions. What you search for is your own business, which is why the DuckDuckGo search doesn’t track you. Ever. Once you start using the new app and browser extension, you’ll quickly notice something: hardly any website currently gets an "A" on privacy. That’s because hardly any website out there truly prioritizes your privacy. Because of these widespread poor privacy practices, too many people believe you simply can’t expect privacy on the Internet. DuckDuckGo disagrees and has made it their mission to set a new standard of trust online. DuckDuckGo wants privacy to be the default, not the exception, and this launch is a major step in that direction. For the last decade, DuckDuckGo has been giving you the ability to search privately, but that privacy was only limited to the DuckDuckGo search box. Now, when you also use the DuckDuckGo browser extension or mobile app, DuckDuckGo will provide you with seamless privacy protection on the websites you visit. DuckDuckGo's goal is to expand this privacy protection over time by adding even more privacy features into this single package. While not all privacy protection can be as seamless, the essentials available today and those that DuckDuckGo will be adding will go a long way to protecting your privacy online, without compromising your Internet experience.

DuckDuckGo Is A Step In The Right Direction For User Privacy

When it comes to private search engines, DuckDuckGo is by far the most popular. If someone has heard of one private search engine, it is probably DuckDuckGo. It began in 2008, and has been growing rapidly ever since. DuckDuckGo currently records over 20 million searches per day. Back in February, I published about the best private alternatives to : this story Google _Private search engines have seen huge growth over the past few years. Until recently, it was unthinkable that anyone…_hackernoon.com The Best Private Search Engines — Alternatives to Google Since then, the post has racked up over 220k views. After a few months of feedback, I realized that Google wasn’t the only search engine people were looking for alternatives to. While DuckDuckGo doesn’t track people using its search engine, it isn’t a perfect, private solution. It doesn’t “follow you around the web with creepy ads”, but for and privacy there are better options. security Search Encrypt uses end-to-end encryption to keep your searches totally private. It started in April 2016 and has been steadily growing. As far as private search engines go, Search Encrypt is probably the most focused on encryption to keep your searches private. Search Encrypt You can use Search Encrypt online or as an installed on your browser. , like most others on this list, is a metasearch engine. This just means that Search Encrypt securely gets its search results from somewhere else, rather than using its own search technology. extension Search Encrypt When you search with Search Encrypt your search term is encrypted locally, and then and transmitted to Search Encrypt’s servers securely. Your personal information is never linked to your searches. After you’ve finished your searching, the encryption key for your search term expires so it is essentially gone forever. How Search Encrypt Keeps Your Searches Private: No Tracking Expiring Browser History SSL/HTTPS Encryption Privacy by Design Transparency StartPage is a great private search option for anyone who prefers Google search results. StartPage calls itself “the world’s most private search engine”. It began as IxQuick in 1998, but later switched to StartPage which was easier to spell and remember, and incorporated Google’s results. StartPage StartPage has a large user base around the world, especially in European countries. It also does a good job of protecting user privacy. It doesn’t store any user data or share information with third parties. Another useful feature StartPage offers is a proxy. This lets you visit any website in your search results through StartPage’s proxy service, further protecting your personal information, like your IP address. StartPage lets users change their preferences and save them in a privacy-friendly way. You can switch your settings as needed, and then StartPage will give you a unique link to use that will give you access to your “version” of StartPage. Qwant Qwant is a search engine based in France that “ who you are or what you are doing.” According to its , Qwant doesn’t track your searches or use your personal data for advertising or other purposes. Qwant has a feature called Qwick search shortcuts that’s similar to . never tries to guess About page DuckDuckGo’s !bangs Qwant offers many filters for finding the right search results faster, including web, news, social, images, videos and music. Another cool feature is called Qwant Lite. This is a special version of Qwant that is optimized for users with older browsers or who are using slower network connections and older computers. Searx is a private metasearch engine that is completely open source. This lets anyone using it to create their own instance. However, if you use your own instance, your search results will be the only one served so it diminishes the privacy. Searx There are a number of public instances that anyone can use, but because it is totally hackable, someone could create a “rogue” instance and potentially log your searches. For that reason, you can use — the official public instance. searx.me Searx’s results page offers many search categories, which helps narrow down your search results. It has IT tab, with results from sites like Stack Overflow and GitHub, which is definitely useful for developers. Searx offers really diverse search results, as it sources results from about 70 different search engines. More About Private Search Engines _Search Encrypt is a privacy based search engine. It is built with privacy-by-design, which means it was made to…_choosetoencrypt.com What is Search Encrypt? Why Should You Use A Private Search Engine? - Search Encrypt Blog _Search neutrality is the idea that a search engine does not limit or influence a person's ability to access information…_choosetoencrypt.com What is Search Neutrality? Is Your Search Engine Biased? - Search Encrypt Blog

Alternative Private Search Engines to DuckDuckGo

Motores de búsqueda privados alternativos a DuckDuckGo

Venezuelans Fight Internet Blocking with Privacy Services like Startpage.com and DuckDuckGo

For whatever reason, you decide to get away from Google (and big tech) these are what you will need : easy to use operating system. install anywhere and not worry about licenses and drivers etc Ubuntu : browser based on chromium Brave : the leading open source Word alternative LibreOffice : search engine. currently comprises of around .5% of the worlds search volume DuckDuckGo : whatsapp replacement. great for calls Telegram or Signal : German/Swiss email provider of your choice (Proton, Tutanota, GMX etc) Email Host : Desktop email and calendar client Thunderbird : social media mewe : for businesses management Nextcloud :  Google Maps Alternative for desktop or mobile OpenStreetMaps instead of youtube LBRY Let us take a quick dive into how each of these works. Ubuntu: First we have to look at the base operating system. If you don't want Apple or Microsoft tracking you need to go Linux. Ubuntu and its derivatives is the most popular and stable Linux flavour that you can install on your PC. Ubuntu Touch is also available for smartphones. You could buy a $200 PinePhone or install it on many android that you already have. Ubuntu touch isn't 100% production ready so please check the following before trying to use it as a daily driver. Click on a device from their website to see which functions currently work. Check out if the has what you need and could run some basic android apps. devices app store Anbox de-googled android: If you don't want to give up your android device you can install a stock android without play services and download apps with pureapk. This is a more stable system then the current Ubuntu Touch operating system. First you need to root your phone and install. Many apps like Whatsapp and GoogleMeet won’t work out of the box. However you should know that many apps that won't install without Google Play Services will work fine in the browser on desktop mode. Brave: Browsers make money by either directing you to a search engine with ads or by targeted ads from the browsers itself. Brave utilizes a brilliant new way of having targeted advertising with collecting any user data. This works by having your browsing data stored locally from which it matches pre-downloaded ads to. The user can choose how many ads displayed per hour and receive crypto coin or choose to opt out of ads entirely. https://brave.com/intro-to-brave-ads/ Brave Search Engine may be the first completely independent, privacy respecting, real search engine DuckDuckGo DDG has been around for about two decades (only 10 years after Google). They're a search engine that simply does not utilize user data. Their search results partially come from its own crawlers and Microsoft Bing which also provides the ads. Their market share makes up somewhere between 1% and 0.5% of the global search engine queries. You could go with a metasearch engine but DDG is the closest to an organic search engine. Email Unfortunately there are no simple self-hosted email solutions. Trust me on this, it's not worth it unless you've successfully done it before. You are best off using a small email provider with a stable business model and integrity. GMX email hosting and thunderbird client make for a nice feature full free email suite. Thunderbird offers end-to-end-encryption so that your email host has no chance of reading your message. Some mail providers (like GMX) only support a browser addon for end-to-end-encrypted mail so use ProtonMail bridge if you want to use e2ee with Thunderbird. Other Desktop tools If you are used to using Microsoft tools like Word or Moviemaker you wont be disappointed using the free alternatives. LibreOffice, Gimp, Audacity, and KdenLive (video editing) are just some of the professional programs available natively on Linux. Most of the big Linux programs also work fine on Windows or Mac. Telegram & Signal These two programs are the new social communication apps of choice to replace Facebook's WhatsApp. The main difference Telegram and Signal is that Telegram is a business. Telegram intends to monetize with ads on large public channels and premium business features. They acknowledge what happened to Whatsapp (bought by Facebook and now sells your private data) and assure users of their privacy and integrity. The client apps are open source so it is easy to assess how Telegram is using your data. As a business, Telegram has grown much faster then Signal and therefore much more likely to be used by the people you know. Signal is an open-source platform which uses peer-to-peer technology for video and voice calls. Because it’s an organization there is no threat of anyone monetizing and changing the system. Their server infrastructure needs to be constantly funded by pro freedom foundation donations and loans. Many companies run their own Signal servers as a way ensuring security and quality service to private employee communications. It can make a difference choosing a client app, for example TELEports app hides your phone number on Telegram by default. Open Street Maps OSM is the best Google Maps alternative for navigation and mapping. I surprised by nice features such as tracking my bike ride without sharing any data. You will have to forgo Streetview, store hours and other specific map data. LBRY Is a peer-to-peer blockchain based video sharing platform that looks like Youtube. Odysee.com is one of the public facing websites you could view it on or you can use a desktop app. Among the downside you might face is: Although LBRY will takedown reported copyrighted and illegal material, YouTube uses AI to watch all videos and filter spam/inappropriate and copyrighted content. This is a tool that LBRY does not have. Also the SEC has launched a lawsuit against their crypto coin offering (considered securities) Mewe Mewe is the Social Network platform similar to Facebook. Wikipedia claims it is popular among conspiracy theorists, and anti-vaxxers. From my experience of 5 connections and 3 followers this is not true. Nextcloud A self hosted office suite that can do all your cloud needs. From file storage to video conferencing Nextcloud is built to replace Office365, Google-drive, zoom and much more. For IT pros For cloud hosting providers I find Linode to be the cheapest, non-political and feature full. There is plenty of authentication There are endless great tools that replace how Google integrates our systems such as pihole and syncthing. There are also great easy tools to hide your own Google footprint such as Wireguard VPN Conclusion Be warned it is an uphill battle avoiding your footprint on the largest corporations ever known to mankind. However monopolies are bad for everyone in the long run, and it up to us to fight it  not to feed it.

The Get Away From Google Guide

We know all the names of the big guys who can give us instant access to information, but with far-reaching data leaks and continued concerns about how our data is shared online, many people are looking for alternative search engines. Here is a primer on search engines and some alternatives you can use in 2020. What is a Search Engine? A engine is a program that searches for results in its database based on your particular search query and delivers results in the form of world wide web pages. Special to quickly cycle through billions of pages to deliver the results it deems most relevant to users. search algorithms are used by search engines Some of the biggest search engines include: Google Yahoo! AOL Bing Ask.com Why Use an Alternative Search Engine? The primary reason people want to use an alternative search engine is due to privacy concerns. According to this report, Google tracks everything you do online while you are signed into your Google account and sometimes even when you are not, including: Every search you perform Every location you go and how long you stay at your destination Every YouTube video you watch Additionally, consumers’ confidence in big tech companies like Google have waned in response to reports that they secretly share users’ . While Google has responded to these concerns by creating a privacy hub that lets users access the information collected on them, the steps to do this are often confusing and users may not know what they are giving permission to do or not do. Furthermore, when users take this action, they are presented with a pop-up message that says that Google will not function as well. private data with third-party advertisers For these reasons, users look to alternative search engines. Best Alternative Search Engines Here are some of the best alternative search engines to Google, Bing, and other big names: 1.     DuckDuckGo is the solution for people who want to protect their privacy while still getting access to information almost instantly. The site does not collect or store users’ personal information, according to its privacy policy. This gives users the peace of mind of knowing their personal information and browsing habits will remain private. Users appreciate the clean interface of this search engine since it is not inundated with many ads. DuckDuckGo 2.     Swisscows markets itself as a family-friendly search engine that also respects users’ privacy. It automatically blocks out violent and pornographic results. It does not collect, store, or track the data of its users. It uses semantics and AI to determine the context of your query and often yields accurate results to users’ questions. Swisscows 3.     StartPage offers a happy compromise between Google’s search ability and users’ privacy. It delivers the same answers as Google but does not track or store users’ data. According to its privacy policy, it strips unnecessary metadata from your query, including your IP address and other identifying information. The search query is then sent to and the search results are returned to you. It also helps to minimize the collection of cookies. It remembers your settings without compromising your privacy. StartPage Google 4.     Search Encrypt does just that – it searches while encrypting your data. Local encryption helps to block your identifiable information so that you are not tracked. Rather than giving you results based on your search history, the program works by giving you results from its network of search partners. Additionally, the program automatically erases your local browsing history after 15 minutes of inactivity. This provides an additional layer of privacy protection in case you share your device with others. Search Encrypt 5.     Disconnect Search This innovative solution allows you the benefit of receiving results from the largest search engines without compromising your privacy. lets you use any search engine. Once you input a search term, the query is rerouted and made anonymous through the program’s servers. It is then run in the search engine you chose. This gives you the best of both worlds: you can use your favorite search engine without having to worry about your privacy. Disconnect Search 6.     Keyhole is an effective search tool that lets you search specifically for social media content and statistics, such as how many times your blog post was shared or tracking the latest hashtag. Keyhole 7.     Social Mention is another effective tool for tracking comments, posts, and other social-media related communications. You can use this site to search across multiple social media networks, as well as blogs, images, news, and more. Social Mention People Search Tools If you are trying to track down a particular person instead of information, you may want to consider using people search tools since search engines are not always the best at finding people. Adding to the last list are: 8.     RecordsFinder lets users find people in a number of ways, including using a reverse email lookup. It is ideal for verifying someone’s background. You can get additional data such as property or business asset information for an extra fee. You can choose a monthly plan to have access to unlimited background reports, criminal record searches, and address and email lookups. RecordsFinder 9.     LinkedIn People Search can help you find new connections on this popular employment site.  You can narrow results by email address, name, language, relationship and other search parameters. With a premium membership, you can also access other search factors, such as years of experience and company size. The LinkedIn People Search 10.  Infotracer is a very good tool to find people. It provides comprehensive services, including searching for people by corralling through more than 43 billion records, using an address lookup to see a history on property, searching criminal record databases, and court records for specific case information. You can also run reverse phone lookups and username search with the service. Infotracer Conclusion Now that you know where you can go to look for and people, you can reserve your general search engine queries only for those purposes that you do not mind sharing with mega tech companies. However, now you can protect your privacy when you want to by using these alternative search engines in 2020. information

10 Alternative Search Engines to Use in 2020

You probably use the web every single day. And a growing concern among every single web user whether or not their information—their identity, banking information, or what they’re browsing—is being tracked by eyes other than their own. And it’s not just hacking threats that we’re all concerned about. Our private information is a billion-dollar goldmine to companies like Facebook, Google, and Amazon, who are stockpiling our data for their own lucrative purposes. There’s no escaping it on our phones, either—over in Apple’s app store are collecting our information. 250 apps And apart from tech giants amassing our data in droves, there’s a lot to fear in the world of government tracking (let’s all remember Edward Snowden). Taking control of can cease hacking attempts, gives those living in countries that might block content the power of accessing what they want, and can give us all the freedom of the fundamental human right that is real privacy. This year more than ever, there’s an overwhelming need to take back the helm of what’s ours. internet privacy In 2020, make your web privacy a central concern. Here are five tools to do just that. Virtual Private Network (VPN) Implementing a VPN is kind of like locking yourself in a near-impenetrable room while the rest of the world carries on out in the open. It’s your very own private chamber, and no one has another like it. VPNs (Virtual Private Networks) are private, controlled networks that give users anonymity by creating a private network from a public internet connection. A VPN protects your privacy by using encryption tech between your device and a VPN server, masking your IP address so that all that you’re doing is untraceable. Setup is a painless process: simply get for access, make an account, then download it to your computer and mobile devices. After logging in, you’ll find that most VPNs offer servers based in other countries that you can connect to. But before implementing a VPN, you have to make sure that they’re not doing the very thing they set out to protect you from. Make sure the VPN you choose has a no-log policy—meaning, it doesn’t track and keep records of user activities. a budget VPN Tor If bulletproof cybersecurity is what you want, then your search ends at , or The Onion Router. Tor is free software that facilitates anonymous transmissions. It’s made up of tens of thousands of volunteer nodes working hard to encrypt your data. Tor After you connect to the Tor network, all sent and received data passes through an arbitrary selection of nodes only engaging through the Tor network. Data leaving your device is encrypted more than once, and an encryption layer is removed each time the data reaches another node until it reaches its final exit node—a process called onion routing. In the highly unlikely event that someone manages to intercept data, it’s either the entry or exit node that they’d find, but not both, making a pathway to a user’s browsing history or data impassable. The unique folds of the Tor system mean that no one (not even the people running the nodes) can see your data. Tails Tails (The Amnesic Incognito Live System) uses the Tor network to protect user anonymity by way of a USB stick or a DVD independent of your computer’s operating system. It’s free software that can be implemented whenever the device is engaged, and on any computer—your own, a friend’s, a library computer, etc. Tails are complete with cutting-edge cryptographic tools that leave no box unchecked when it comes to protecting your data. It comes with secure, built-in applications for trustworthy web browsing, emailing, messaging, and more, and is easily disengaged when a user wants to return to their computer’s own OS. DuckDuckGo Even if you’re in private browsing mode, some search engines are still tracking your every move. eliminates all tracking when you’re searching the web, for free. DuckDuckGo DuckDuckGo is essentially a private, secure Google—a search engine that doesn’t spy on your data. You can use to browse your queries, or you can add it as your default browser, which will allow you to use it when searching from your address bar. And the great thing about DuckDuckGo is that what comes up is pretty much indiscernible from what you’d find with a Google search. DuckDuckGo’s search page ProtonMail Somewhere around are sent and received every day. And according to an OMC study, over of those emails are tracked. Privacy concerns for email users are a serious issue, and ProtonMail is a free, encrypted email service that promises users to complete privacy with client-side encryption. This means no one, not even ProtonMail, will have eyes on your emails. 269 billion emails 40 percent ProtonMail is a Swiss-based startup whose infrastructure occupies Europe’s most robust data centre—all safeguarded under 1000 meters of solid rock. It’s fully compatible with other email providers and is easy to use without the need for software.

4 Important Web Security Tools During Privacy Crisis

So, you're itching to publish a story on HackerNoon but feeling a bit lost? Don't fret! We've put together this step-by-step guide with all the information you'll need to go from draft to top story in no time. Let's dive in! TL:DR To Submit a story to HackerNoon, please follow the Below Checklist: Log in to Hacker Noon via Hackernoon.com/login. If you’re struggling to create an account at HackerNoon, read this blog post or visit our help page.
Click on Start Writing to the left of your thumbnail (or go directly there).
Write or upload a great tech story - detailed instructions for editor 3.0 here.
Click Story Settings in the upper right-hand corner.
Enter 8 tags (guideline).
Upload a feature image (guideline).
Enter your meta tag so search engines can easily find you (guideline).
Confirm that you have the rights to submit the content and that all vested interests of companies mentioned in the story have been disclosed.
Select Submit Story for Review.
You will then receive an email to confirm that we have received your submission.
A Staff Editor should review within 3 business days..ish. See full guidelines here.
If accepted, we’ll distribute, distribute, and distribute your tech story. See how we distribute things here. https://www.youtube.com/watch?v=O5p0nUvIZ6A&embedable=true Writers meet the draft page! Let's set the scene: you've been an avid reader of HackerNoon for some time now and finally decided it's time to share your own content here. So you hit the "Start Writing" button sitting at the top right corner of your screen. Boom! It's writing time - and here's what you'll find on our editor 3.0: If you’re looking to import a story, learn more here, and consult our republishing guidelines here. 1. Titles When crafting your new story in our editor, you've got two choices for titles: you can either come up with your own or let our system generate suggestions for you. To enter your own title, simply type it into the designated space and use our auto-capitalization feature to ensure proper formatting—click the robot icon to toggle this feature on or off. If you're feeling stuck, click the lightbulb icon for a suggestion. Just make sure your text body contains enough content for our system to detect the story's theme, and ensure the title space is empty before clicking "generate headline". Once you've added your title, your draft will be automatically saved, so remember to include some text. Remember, your title should accurately reflect the essence of your story and include relevant keywords to help readers discover it. Learn more here. 2. Formatting Features In our editor, you've got all the tools at your fingertips to format your text just how you want it: bold, overline, underline, lists, links, headlines, quotes, code, and more. To do so select your text and choose your desired options. Alternatively, you can click the + button on the left of your text line and pick an option from the list, which contains some options seen above and others like tables, images, links, dividers, info notices, and more. Here’s how it looks: 3. Embeds There are three main things you can embed in your story: images, videos, and links. Starting with images, you've got a range of choices: import from your device, import an image from a URL, grab one from an image bank, embed a GIF, or generate an AI image. Simply choose your preferred option, and voila! Your image will appear in your draft. When it comes to videos, you've can import them as you would images or embed YouTube links. Click the + button, navigate to the link option, paste your URL, and hit enter. Then, you'll encounter a prompt asking if you want to embed the link. For YouTube videos, you'll have the choice to add just the video, the transcription, or both - it will look something like this: Lastly, links: it’s very similar to embedding YouTube videos, except you can link stories, code, and other external sources. Here’s how: Embedding also extends to Twitter/X threads. Onto The Story Settings Now that you have the gist of what you can do with your copy on our editor, here’s the rundown on the story settings fields you’ll need to fill out before submitting the story for editorial review: Add all of your story’s 8 tags: Tags help Google, Bing, DuckDuckGo, and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. 2. Open Story Settings: At the top of your story settings you’ll see the number of words written - check this before submitting. One of our editorial guidelines stipulates a minimum word count for publication eligibility. Upload a featured image: Choose one from your device, explore image banks, and GIFs, or ask AI to create a new one. Customize how you want comments managed for your story: Decide whether you want them to undergo editorial review or be approved instantly.
Add your metadescription: Help web crawlers to index and humans to read
Craft a TL:DR: type between 300 and 500 characters or have our AI generate a TL:DR for you Pick Emoji Credibility Indicators: These indicators help you provide additional information relating to your story, giving readers more context.
Is your story original? If it's a republication, hit "No" and drop the URL where it first went live; if it's fresh content, just tap "Yes". When in doubt, consult our republishing guidelines here. Once you've wrapped up all these steps, keep an eye out for a button that pops up at the bottom prompting you to "Submit Your Story for Review!" If you're feeling good about your story, just give it a click. Soon after, you'll get an email confirming that we've received your submission. A Staff Editor should review it within 3 business days..ish, and if accepted, we’ll distribute, distribute, and distribute your tech story. NEW! On your draft, you’ll see the Submit Story button left in three different places:

At the bottom of your story settings
At the top corner of your screen
At the bottom right corner of your screen

Once you select the checkbox that matches your story's originality, these buttons will become active. Additional tools In the story settings, you'll find some extra goodies! You can whip up sticky notes and shoot a message to an editor for some writing support. Check out how it looks below: And there you have it! You're now equipped with the tools to transition from draft to publication - no reason not to hit that submit button and share your work with the world! So, you're itching to publish a story on HackerNoon but feeling a bit lost? Don't fret! We've put together this step-by-step guide with all the information you'll need to go from draft to top story in no time. Let's dive in! from draft to top story TL:DR To Submit a story to HackerNoon, please follow the Below Checklist: To Submit a story to HackerNoon, please follow the Below Checklist: Log in to Hacker Noon via Hackernoon.com/login. If you’re struggling to create an account at HackerNoon, read this blog post or visit our help page. Click on Start Writing to the left of your thumbnail (or go directly there). Write or upload a great tech story - detailed instructions for editor 3.0 here. Click Story Settings in the upper right-hand corner. Enter 8 tags (guideline). Upload a feature image (guideline). Enter your meta tag so search engines can easily find you (guideline). Confirm that you have the rights to submit the content and that all vested interests of companies mentioned in the story have been disclosed. Select Submit Story for Review. You will then receive an email to confirm that we have received your submission. A Staff Editor should review within 3 business days..ish. See full guidelines here. If accepted, we’ll distribute, distribute, and distribute your tech story. See how we distribute things here. Log in to Hacker Noon via Hackernoon.com/login . If you’re struggling to create an account at HackerNoon, read this blog post or visit our help page . Hackernoon.com/login read this blog post help page Click on Start Writing to the left of your thumbnail (or go directly there ). there Write or upload a great tech story - detailed instructions for editor 3.0 here . upload a great tech story here Click Story Settings in the upper right-hand corner. Enter 8 tags ( guideline ). guideline Upload a feature image ( guideline ). guideline Enter your meta tag so search engines can easily find you ( guideline ). guideline Confirm that you have the rights to submit the content and that all vested interests of companies mentioned in the story have been disclosed. Select Submit Story for Review. You will then receive an email to confirm that we have received your submission. A Staff Editor should review within 3 business days..ish. See full guidelines here . here If accepted, we’ll distribute, distribute, and distribute your tech story. See how we distribute things here . here https://www.youtube.com/watch?v=O5p0nUvIZ6A&embedable=true https://www.youtube.com/watch?v=O5p0nUvIZ6A&embedable=true Writers meet the draft page! Writers meet the draft page! Let's set the scene: you've been an avid reader of HackerNoon for some time now and finally decided it's time to share your own content here. So you hit the "Start Writing" button sitting at the top right corner of your screen. Boom! It's writing time - and here's what you'll find on our editor 3.0 : editor 3.0 If you’re looking to import a story, learn more here, and consult our republishing guidelines here. If you’re looking to import a story, learn more here , and consult our republishing guidelines here . learn more here here 1. Titles Titles When crafting your new story in our editor, you've got two choices for titles: you can either come up with your own or let our system generate suggestions for you. To enter your own title, simply type it into the designated space and use our auto-capitalization feature to ensure proper formatting—click the robot icon to toggle this feature on or off. If you're feeling stuck, click the lightbulb icon for a suggestion. Just make sure your text body contains enough content for our system to detect the story's theme, and ensure the title space is empty before clicking " generate headline ". generate headline Once you've added your title, your draft will be automatically saved, so remember to include some text. Remember, your title should accurately reflect the essence of your story and include relevant keywords to help readers discover it. Learn more here. Remember, your title should accurately reflect the essence of your story and include relevant keywords to help readers discover it. Learn more here . here 2. Formatting Features In our editor, you've got all the tools at your fingertips to format your text just how you want it: bold, overline, underline, lists, links, headlines , quotes , code , and more. To do so select your text and choose your desired options. format your text headlines quotes code Alternatively, you can click the + button on the left of your text line and pick an option from the list, which contains some options seen above and others like tables, images, links, dividers, info notices, and more. Here’s how it looks: 3. Embeds There are three main things you can embed in your story: images, videos , and links. videos Starting with images, you've got a range of choices: import from your device, import an image from a URL, grab one from an image bank, embed a GIF , or generate an AI image . Simply choose your preferred option, and voila! Your image will appear in your draft. embed a GIF generate an AI image When it comes to videos, you've can import them as you would images or embed YouTube links . Click the + button , navigate to the link option, paste your URL, and hit enter. Then, you'll encounter a prompt asking if you want to embed the link. For YouTube videos, you'll have the choice to add just the video, the transcription, or both - it will look something like this: embed YouTube links + button Lastly, links: it’s very similar to embedding YouTube videos, except you can link stories, code, and other external sources . Here’s how: link stories, code, and other external sources Embedding also extends to Twitter/X threads. Embedding also extends to Twitter/X threads . Twitter/X threads Onto The Story Settings Now that you have the gist of what you can do with your copy on our editor, here’s the rundown on the story settings fields you’ll need to fill out before submitting the story for editorial review: Add all of your story’s 8 tags: Tags help Google, Bing, DuckDuckGo, and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags: Tags help Google, Bing, DuckDuckGo, and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags: Tags help Google , Bing , DuckDuckGo , and friends find your content, to serve it to users searching for those terms. Plus, tags help HackerNoon distribute your content to top trafficked pages, as well as the most subscribed tags via our customizable newsletter, The Tech Brief. Add all of your story’s 8 tags : Add all of your story’s 8 tags Google Bing DuckDuckGo 2. Open Story Settings : At the top of your story settings you’ll see the number of words written - check this before submitting. One of our editorial guidelines stipulates a minimum word count for publication eligibility. Open Story Settings editorial guidelines Upload a featured image: Choose one from your device, explore image banks, and GIFs, or ask AI to create a new one. Upload a featured image: Choose one from your device, explore image banks, and GIFs, or ask AI to create a new one. Upload a featured image: Customize how you want comments managed for your story: Decide whether you want them to undergo editorial review or be approved instantly. Add your metadescription: Help web crawlers to index and humans to read Craft a TL:DR: type between 300 and 500 characters or have our AI generate a TL:DR for you Customize how you want comments managed for your story: Decide whether you want them to undergo editorial review or be approved instantly. Customize how you want comments managed for your story: Customize how you want comments managed for your story: Add your metadescription: Help web crawlers to index and humans to read Add your metadescription : Add your metadescription Craft a TL:DR: type between 300 and 500 characters or have our AI generate a TL:DR for you Craft a TL:DR: Pick Emoji Credibility Indicators: These indicators help you provide additional information relating to your story, giving readers more context. Is your story original? If it's a republication, hit "No" and drop the URL where it first went live; if it's fresh content, just tap "Yes". When in doubt, consult our republishing guidelines here. Pick Emoji Credibility Indicators: These indicators help you provide additional information relating to your story, giving readers more context. Pick Emoji Credibility Indicators : Pick Emoji Credibility Indicators Is your story original? If it's a republication, hit "No" and drop the URL where it first went live; if it's fresh content, just tap "Yes". When in doubt, consult our republishing guidelines here . Is your story original? here Once you've wrapped up all these steps, keep an eye out for a button that pops up at the bottom prompting you to " Submit Your Story for Review! " If you're feeling good about your story, just give it a click. Soon after, you'll get an email confirming that we've received your submission. A Staff Editor should review it within 3 business days..ish, and if accepted, we’ll distribute, distribute, and distribute your tech story. Submit Your Story for Review! NEW! On your draft, you’ll see the Submit Story button left in three different places: At the bottom of your story settings
At the top corner of your screen
At the bottom right corner of your screen Once you select the checkbox that matches your story's originality, these buttons will become active. NEW! On your draft, you’ll see the Submit Story button left in three different places: Submit Story At the bottom of your story settings At the top corner of your screen At the bottom right corner of your screen At the bottom of your story settings At the top corner of your screen At the bottom right corner of your screen Once you select the checkbox that matches your story's originality, these buttons will become active. Additional tools In the story settings, you'll find some extra goodies! You can whip up sticky notes and shoot a message to an editor for some writing support. Check out how it looks below: And there you have it! You're now equipped with the tools to transition from draft to publication - no reason not to hit that submit button and share your work with the world!

From Desk to HackerNoon: Your Ultimate Guide on How to Publish a Story

ডেস্ক থেকে হ্যাকারনুন পর্যন্ত: কীভাবে একটি গল্প প্রকাশ করবেন তার চূড়ান্ত গাইড

Vom Schreibtisch zum HackerNoon: Ihr ultimativer Leitfaden zum Veröffentlichen einer Story

Del escritorio a HackerNoon: su guía definitiva sobre cómo publicar una historia

Du bureau à HackerNoon : votre guide ultime sur la façon de publier une histoire

डेस्क से हैकरनून तक: कहानी प्रकाशित करने के बारे में आपकी अंतिम मार्गदर्शिका

デスクから HackerNoon へ: ストーリーを公開するための究極のガイド

데스크에서 HackerNoon까지: 스토리 게시 방법에 대한 최고의 가이드

From Desk to HackerNoon: seu guia definitivo sobre como publicar uma história

От стола до HackerNoon: ваше полное руководство о том, как опубликовать историю

Masadan HackerNoon'a: Bir Hikayenin Nasıl Yayınlanacağına İlişkin Temel Kılavuzunuz

Từ bàn làm việc đến HackerNoon: Hướng dẫn cơ bản về cách xuất bản một câu chuyện

从办公桌到 HackerNoon：如何发布故事的终极指南

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References DISCUSSION Our study examined whether there are differences in the sources of the top search results between Google and alternative search engines based on popular search queries. An evaluation of root domains of the most popular sources shows only a small overlap between Google and the alternative search engines (RQ1). Overall, we found an overlap of 27% to 28% between Google and the alternatives in German results, and in the US, the overlap ranges from 24% to 25%. There is a significantly higher overlap between the alternative search engines of about 63% to 70% in German results and 62% to 65% in US results. This may be explained by all three alternative search engines using Bing's index at least in part. However, our findings show that Metager consistently has the highest overlap with Bing, going up to 78% and only overlapping as much as 64% with DuckDuckGo. The lower overlap of Google with the alternative search engines had already been shown in the studies of Agrawal et al. (2016) and Makhortykh et al. (2020). Our study provides further evidence for this. When looking at the uniqueness of sources across all German queries, we found that the search engines returned a similar total number of sources, ranging from 2,693 to 2,841. However, for the US queries, the variety in Google was noticeably higher, with over 4,000 total sources compared to around 3,500 of the alternatives. The most popular domain was Wikipedia, followed by sources we classified as News services (RQ2). This is likely exacerbated by selecting Google Trends as the source of our search queries, which usually includes queries related to popular news stories, sports, and celebrities. Still, it is consistent with previous studies' findings (Steiner et al., 2022) which already showed that Wikipedia and news made up the majority of search results. Furthermore, in our research, Wikipedia and news sources were the top domains across all results and the most frequent sources for each search engine individually. When comparing the result sets of the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US, it is interesting to see that news sources are far more prevalent in German results, with social media being very infrequent. On the other hand, the US results had more social media websites. Interestingly, in the US results, YouTube was in the top 10 most popular domains in all search engines but Google. The same was the case for the top 50 domains in German results. This is unexpected because YouTube is a subsidiary of Google. However, this finding may be explained by the fact that we only collected organic search results, and Google might be using universal search results to display YouTube results. Another interesting difference is the greater preference of Wikipedia in Google in US results (1,892, 10,2% of all domains) compared to German results (658, 4% of all domains). Furthermore, the second and third most popular sources on Google are Instagram and Facebook for the US results, while they are not even in the top 10 of German Google sources. Finally, in terms of what is missing from Google in the US results, it is notable that Fox News is not found in the top 50 sources, while it is present in all of the alternatives. The concentration of sources and source diversity showed a tendency for only a few root domains to make up a large share of search results. The Gini Index values of 0.73 and 0.79 in Germany and the United States, respectively, are a clear indicator (RQ3). This is consistent with findings from previous studies (Höchstötter & Lewandowski, 2009) that showed that only a few top sources dominate the search results in search engines. Of course, our study is not without limitations. First, the selection of search queries is the most significant factor in compiling the data that was evaluated. Even though the number of queries is high and there is some diversity in the queries, the topics are almost always focused on news, celebrities, and sports, which inevitably leads to many news sources in the search results. A refined approach to selecting search queries would be appropriate for a more accurate evaluation of source diversity. For example, this could be achieved by focusing on socially controversial topics and choosing the queries accordingly. Further limitations arise in the evaluation of source types. The classification we used in this study is very broad. A more precise classification would help make statements about the actual kinds of sources and thus to determine even more precisely which preferences and biases exist in different search engines. For example, grouping sources according to seriousness and reliability could serve as an explanation for the selection of sources to be displayed in search results. Regarding the search results collected in this study, another limitation is that only organic search results were considered. We did not consider advertisements or universal search results, although these have a strong influence on what users see on the SERP. The relevance of the search results was also not considered. This paper is available on arxiv under CC 4.0 license.
Lead image by Edho Pratama on Unsplash Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References DISCUSSION Our study examined whether there are differences in the sources of the top search results between Google and alternative search engines based on popular search queries. An evaluation of root domains of the most popular sources shows only a small overlap between Google and the alternative search engines (RQ1). Overall, we found an overlap of 27% to 28% between Google and the alternatives in German results, and in the US, the overlap ranges from 24% to 25%. There is a significantly higher overlap between the alternative search engines of about 63% to 70% in German results and 62% to 65% in US results. This may be explained by all three alternative search engines using Bing's index at least in part. However, our findings show that Metager consistently has the highest overlap with Bing, going up to 78% and only overlapping as much as 64% with DuckDuckGo. The lower overlap of Google with the alternative search engines had already been shown in the studies of Agrawal et al. (2016) and Makhortykh et al. (2020). Our study provides further evidence for this. When looking at the uniqueness of sources across all German queries, we found that the search engines returned a similar total number of sources, ranging from 2,693 to 2,841. However, for the US queries, the variety in Google was noticeably higher, with over 4,000 total sources compared to around 3,500 of the alternatives. The most popular domain was Wikipedia, followed by sources we classified as News services (RQ2). This is likely exacerbated by selecting Google Trends as the source of our search queries, which usually includes queries related to popular news stories, sports, and celebrities. Still, it is consistent with previous studies' findings (Steiner et al., 2022) which already showed that Wikipedia and news made up the majority of search results. Furthermore, in our research, Wikipedia and news sources were the top domains across all results and the most frequent sources for each search engine individually. When comparing the result sets of the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US, it is interesting to see that news sources are far more prevalent in German results, with social media being very infrequent. On the other hand, the US results had more social media websites. Interestingly, in the US results, YouTube was in the top 10 most popular domains in all search engines but Google. The same was the case for the top 50 domains in German results. This is unexpected because YouTube is a subsidiary of Google. However, this finding may be explained by the fact that we only collected organic search results, and Google might be using universal search results to display YouTube results. Another interesting difference is the greater preference of Wikipedia in Google in US results (1,892, 10,2% of all domains) compared to German results (658, 4% of all domains). Furthermore, the second and third most popular sources on Google are Instagram and Facebook for the US results, while they are not even in the top 10 of German Google sources. Finally, in terms of what is missing from Google in the US results, it is notable that Fox News is not found in the top 50 sources, while it is present in all of the alternatives. The concentration of sources and source diversity showed a tendency for only a few root domains to make up a large share of search results. The Gini Index values of 0.73 and 0.79 in Germany and the United States, respectively, are a clear indicator (RQ3). This is consistent with findings from previous studies (Höchstötter & Lewandowski, 2009) that showed that only a few top sources dominate the search results in search engines. Of course, our study is not without limitations. First, the selection of search queries is the most significant factor in compiling the data that was evaluated. Even though the number of queries is high and there is some diversity in the queries, the topics are almost always focused on news, celebrities, and sports, which inevitably leads to many news sources in the search results. A refined approach to selecting search queries would be appropriate for a more accurate evaluation of source diversity. For example, this could be achieved by focusing on socially controversial topics and choosing the queries accordingly. Further limitations arise in the evaluation of source types. The classification we used in this study is very broad. A more precise classification would help make statements about the actual kinds of sources and thus to determine even more precisely which preferences and biases exist in different search engines. For example, grouping sources according to seriousness and reliability could serve as an explanation for the selection of sources to be displayed in search results. Regarding the search results collected in this study, another limitation is that only organic search results were considered. We did not consider advertisements or universal search results, although these have a strong influence on what users see on the SERP. The relevance of the search results was also not considered. This paper is available on arxiv under CC 4.0 license. Lead image by Edho Pratama on Unsplash This paper is available on arxiv under CC 4.0 license. available on arxiv Lead image by Edho Pratama on Unsplash Edho Pratama on Unsplash

Differences in Top Search Results Between Google and Alternative Search Engines

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References RESULTS Classification of domains Since we are working with sources from two different countries and the type of content behind some domains might not be immediately recognizable, we manually created a general set of categories based on the top 50 domains found across all results. Following this, we manually classified the 50 most popular domains for both the German and US results (see Table 1). When comparing the top 50 domains in the German results, News service dominates with 54%, while Movies & Entertainment and Sports make up 18% and 14%, respectively. In the US, 34% of the top 50 domains are Sports websites, with News services following close behind at 30%. Movies & Entertainment only make up 12%. These are the overall distributions of domain classes across all search engines; below, we look at the differences between the search engines. Variety of domains A comparison of the number of root domains in the search results of Google, Bing, DuckDuckGo, and Metager in Germany shows that Google has the greatest diversity by a small margin (see Figure 2). Overall, the values are very similar. However, it is noticeable that Google has the greatest variety of domains, especially in the first three positions. Interestingly, the greater diversity of Google's sources is even more pronounced in the US results. To examine the differences more closely, we look at the numbers below. Table 2 shows the cumulative frequencies for the German results. The number of unique root domains converges at the fifth position. We found 2,841 unique domains in Google's results, 2,783 unique domains for Bing, 2,707 unique domains for DuckDuckGo, and 2,683 unique domains for Metager in Germany. There are differences when comparing the search results in Germany and the US. It is notable that, in contrast to Germany, in the US results, there are only minor differences in the first positions. However, Google also shows the greatest variety of root domains in the search results in the US. This is more evident than in the German results (see Table 3). Overall, we found 4,085 unique domains in Google, 3,602 in Bing, 3,579 in DuckDuckGo, and 3,500 in Metager. Popular domains When comparing the top domains for each search engine across all German search results collected, we find a clear preference for Wikipedia in all of them (see Table 4). While Wikipedia is the most popular domain in all search engines, the frequency in the Google results is significantly lower, with only 658 compared to the number of occurrences in Bing being 1,948, in Metager 1,878, and in DuckDuckGo 1,752. The other domains in the top 10 across the search engines are News services (Google: 7, Bing: 4, DuckDuckGo: 3, Metager: 4). Sports make up 2 out of 10 for all but Google, with only one sports website. Surprisingly, Instagram is in the top 10 for DuckDuckGo and Metager, even though in Table 1, we showed that domains of the class Social Media only make up 4% of top domains in the German results. The same goes for Amazon, which is the second most frequent domain in DuckDuckGo, even though in the overall results, E-Commerce websites only make up 2%. Contrarily, in the US results, Wikipedia has the highest number of occurrences in Google with 1,892 compared to Bing's 1,388, Metager's 1,304, and DuckDuckGo's 1,287 (see Table 5). Still, what remains the same is that Wikipedia is the most popular domain across all search engines. Another difference in the US results is that Social Media sources are much more prevalent across all search engines, especially in Google, with Instagram as the second most frequent domain, Facebook as the third, and Twitter as the sixth. This is surprising when considering that Social Media domains only make up 6% of the top domains for the US results (see Table 1). Another finding is that YouTube, a subsidiary of Google, is the top 8th domain for all three alternatives but not for Google. Again, 30% of the top domains for Google, DuckDuckGo, and Metager are News services. For Bing, it is 40%. For each search engine, Sports websites make up 20% of domains. Exclusivity of domains This section compares the top 50 domains for each search engine to determine what source Google and its alternatives might exclusively offer to the user. Table 6 shows the domains found solely in Google's top 50 domains list for the German queries. Out of these 20 domains, eight are Sports websites, six are News services, and four belong to the Entertainment & Movies class, with one each for E-Commerce and Government websites. On the contrary, table 7 shows the list of domains found in the top 50 domains of all three alternatives but not in Google. These would be the domains missed by users who only use Google. Out of the 13 domains, four are News services, followed by three Movies & Entertainment and two Celebrities sources. Interestingly, Instagram and YouTube are missing from Google, as well. When implementing the same evaluation for the US results, we find that 17 domains are exclusively found in Google's top 50 domains, with five domains classified as Sports, four as Movies & Entertainment, another four as News services, three as Social Media and one as Celebrities (see Table 8). Notable is that social media giants Facebook and TikTok are only found in Google and not in the alternatives. On the opposite end, 13 domains are not found in the US results on Google (see Table 9). Six of them are News services, three are websites about Celebrities, two are about Movies & Entertainment, and one each are Sports and E-Commerce websites. Interestingly, Amazon is missing in Google's results, as well as msn.com, a service from Microsoft, like Bing itself. Distribution of domains Next, we look at the Gini coefficient to measure the concentration and statistical dispersion of root domains in search engines. The distributions across all search engines in both German and US results show very similar values. The results range from 0.77 to 0.79 and 0.73 to 0.76 for German and US results, respectively (see Figure 3). Overall, this means that there is an imbalance in the distribution of results from root domains and that some top sources dominate the search results for both countries. Similarity of domains Comparing the similarities of the top 10 German results, we find that Bing and Metager are most similar in terms of their top results, with 70%, followed by 64% overlap between DuckDuckGo and Metager and 63% between Bing and DuckDuckGo. However, when comparing the alternative search engines to Google, we find that the highest overlap is between Google and Bing with 28% and slightly lower ones with DuckDuckGo and Metager at 27%. Interestingly, the top 1 result overlap is higher between Google and Bing (31%) than the overlap between Bing and DuckDuckGo (15%). The results show a mean overlap of 30% of top 1, 40% of top 3, and 47% of top 10 results (table 10). This indicates that when looking at the entire first search results page, nearly half of the domains are the same in all search engines considered. For search engine users, this means that in addition to Google, this would also allow them to see search results that they would otherwise miss if they used the alternative search engine The same evaluation for the US yields similar results. Again, the overlap between Bing and Metager is the highest, but the margin between this pair and the other alternative pairings has been reduced. When looking at the top 10 results, Bing and Metager overlap by 65%, Bing and DuckDuckGo by 64%, and DuckDuckGo and Metager by 62%. While Google's overlap with the other search engines is lower in the US results, again, the pair of Google and Bing is slightly higher (25%) than the 24% overlap between Google and the other two alternatives (see Table 11). Narrowing the results down to the top 3, the overlap between Bing and DuckDuckGo increases minimally to 65% and remains the same for DuckDuckGo and Metager at 62%. Here, the overlap between Google and Bing is lower than in the German results, with only 26%. Furthermore, the top 1 result overlap between Google and Bing is at 29%, which is also lower than in the German results. Generally, compared to the German results, there is a higher overlap in the top 1 position for the pairs Google and DuckDuckGo (20%), Bing and DuckDuckGo (50%), and DuckDuckGo and Metager (47%). The averaged results make this trend clearer because the overlaps for the top 1, top 3, and top 10 results are similar, with 41%, 46%, and 44%, respectively. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References RESULTS Classification of domains Since we are working with sources from two different countries and the type of content behind some domains might not be immediately recognizable, we manually created a general set of categories based on the top 50 domains found across all results. Following this, we manually classified the 50 most popular domains for both the German and US results (see Table 1). When comparing the top 50 domains in the German results, News service dominates with 54%, while Movies & Entertainment and Sports make up 18% and 14%, respectively. In the US, 34% of the top 50 domains are Sports websites, with News services following close behind at 30%. Movies & Entertainment only make up 12%. These are the overall distributions of domain classes across all search engines; below, we look at the differences between the search engines. Variety of domains A comparison of the number of root domains in the search results of Google, Bing, DuckDuckGo, and Metager in Germany shows that Google has the greatest diversity by a small margin (see Figure 2). Overall, the values are very similar. However, it is noticeable that Google has the greatest variety of domains, especially in the first three positions. Interestingly, the greater diversity of Google's sources is even more pronounced in the US results. To examine the differences more closely, we look at the numbers below. Table 2 shows the cumulative frequencies for the German results. The number of unique root domains converges at the fifth position. We found 2,841 unique domains in Google's results, 2,783 unique domains for Bing, 2,707 unique domains for DuckDuckGo, and 2,683 unique domains for Metager in Germany. There are differences when comparing the search results in Germany and the US. It is notable that, in contrast to Germany, in the US results, there are only minor differences in the first positions. However, Google also shows the greatest variety of root domains in the search results in the US. This is more evident than in the German results (see Table 3). Overall, we found 4,085 unique domains in Google, 3,602 in Bing, 3,579 in DuckDuckGo, and 3,500 in Metager. Popular domains When comparing the top domains for each search engine across all German search results collected, we find a clear preference for Wikipedia in all of them (see Table 4). While Wikipedia is the most popular domain in all search engines, the frequency in the Google results is significantly lower, with only 658 compared to the number of occurrences in Bing being 1,948, in Metager 1,878, and in DuckDuckGo 1,752. The other domains in the top 10 across the search engines are News services (Google: 7, Bing: 4, DuckDuckGo: 3, Metager: 4). Sports make up 2 out of 10 for all but Google, with only one sports website. Surprisingly, Instagram is in the top 10 for DuckDuckGo and Metager, even though in Table 1, we showed that domains of the class Social Media only make up 4% of top domains in the German results. The same goes for Amazon, which is the second most frequent domain in DuckDuckGo, even though in the overall results, E-Commerce websites only make up 2%. Contrarily, in the US results, Wikipedia has the highest number of occurrences in Google with 1,892 compared to Bing's 1,388, Metager's 1,304, and DuckDuckGo's 1,287 (see Table 5). Still, what remains the same is that Wikipedia is the most popular domain across all search engines. Another difference in the US results is that Social Media sources are much more prevalent across all search engines, especially in Google, with Instagram as the second most frequent domain, Facebook as the third, and Twitter as the sixth. This is surprising when considering that Social Media domains only make up 6% of the top domains for the US results (see Table 1). Another finding is that YouTube, a subsidiary of Google, is the top 8th domain for all three alternatives but not for Google. Again, 30% of the top domains for Google, DuckDuckGo, and Metager are News services. For Bing, it is 40%. For each search engine, Sports websites make up 20% of domains. Exclusivity of domains This section compares the top 50 domains for each search engine to determine what source Google and its alternatives might exclusively offer to the user. Table 6 shows the domains found solely in Google's top 50 domains list for the German queries. Out of these 20 domains, eight are Sports websites, six are News services, and four belong to the Entertainment & Movies class, with one each for E-Commerce and Government websites. On the contrary, table 7 shows the list of domains found in the top 50 domains of all three alternatives but not in Google. These would be the domains missed by users who only use Google. Out of the 13 domains, four are News services, followed by three Movies & Entertainment and two Celebrities sources. Interestingly, Instagram and YouTube are missing from Google, as well. When implementing the same evaluation for the US results, we find that 17 domains are exclusively found in Google's top 50 domains, with five domains classified as Sports, four as Movies & Entertainment, another four as News services, three as Social Media and one as Celebrities (see Table 8). Notable is that social media giants Facebook and TikTok are only found in Google and not in the alternatives. On the opposite end, 13 domains are not found in the US results on Google (see Table 9). Six of them are News services, three are websites about Celebrities, two are about Movies & Entertainment, and one each are Sports and E-Commerce websites. Interestingly, Amazon is missing in Google's results, as well as msn.com, a service from Microsoft, like Bing itself. Distribution of domains Next, we look at the Gini coefficient to measure the concentration and statistical dispersion of root domains in search engines. The distributions across all search engines in both German and US results show very similar values. The results range from 0.77 to 0.79 and 0.73 to 0.76 for German and US results, respectively (see Figure 3). Overall, this means that there is an imbalance in the distribution of results from root domains and that some top sources dominate the search results for both countries. Similarity of domains Comparing the similarities of the top 10 German results, we find that Bing and Metager are most similar in terms of their top results, with 70%, followed by 64% overlap between DuckDuckGo and Metager and 63% between Bing and DuckDuckGo. However, when comparing the alternative search engines to Google, we find that the highest overlap is between Google and Bing with 28% and slightly lower ones with DuckDuckGo and Metager at 27%. Interestingly, the top 1 result overlap is higher between Google and Bing (31%) than the overlap between Bing and DuckDuckGo (15%). The results show a mean overlap of 30% of top 1, 40% of top 3, and 47% of top 10 results (table 10). This indicates that when looking at the entire first search results page, nearly half of the domains are the same in all search engines considered. For search engine users, this means that in addition to Google, this would also allow them to see search results that they would otherwise miss if they used the alternative search engine The same evaluation for the US yields similar results. Again, the overlap between Bing and Metager is the highest, but the margin between this pair and the other alternative pairings has been reduced. When looking at the top 10 results, Bing and Metager overlap by 65%, Bing and DuckDuckGo by 64%, and DuckDuckGo and Metager by 62%. While Google's overlap with the other search engines is lower in the US results, again, the pair of Google and Bing is slightly higher (25%) than the 24% overlap between Google and the other two alternatives (see Table 11). Narrowing the results down to the top 3, the overlap between Bing and DuckDuckGo increases minimally to 65% and remains the same for DuckDuckGo and Metager at 62%. Here, the overlap between Google and Bing is lower than in the German results, with only 26%. Furthermore, the top 1 result overlap between Google and Bing is at 29%, which is also lower than in the German results. Generally, compared to the German results, there is a higher overlap in the top 1 position for the pairs Google and DuckDuckGo (20%), Bing and DuckDuckGo (50%), and DuckDuckGo and Metager (47%). The averaged results make this trend clearer because the overlaps for the top 1, top 3, and top 10 results are similar, with 41%, 46%, and 44%, respectively. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Domain Classification: Analyzing Source Types in Search Engine Results

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References OBJECTIVES AND RESEARCH QUESTIONS Our study addresses the following research questions: 1. Do top results from alternative search engines differ from Google's in regard to the number of unique sources? 2. Do top results from alternative search engines differ from Google's in regard to top sources? 3. Do top results from alternative search engines differ from Google's in regard to source concentration, i.e., are results distributed over more or fewer sources in different search engines? To answer these questions, we selected three alternative search engines to compare to Google. Aside from Bing, which is the biggest competitor to Google, we chose DuckDuckGo and Metager. DuckDuckGo uses results from Bing, Yahoo, and Yandex, and has the added benefit of advanced privacy settings such as non-personalized results. Metager, a German meta-search engine, aggregates results from several search engines, including Bing and Yandex. As such, either search engine might provide a more varied set of sources. Since the alternative search engines each have an index different from Google's, the comparison will give insights into what domains are favored by Google and which domains are excluded. Also, we will gain insights into the differences in ranking between them. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References OBJECTIVES AND RESEARCH QUESTIONS Our study addresses the following research questions: 1. Do top results from alternative search engines differ from Google's in regard to the number of unique sources? 2. Do top results from alternative search engines differ from Google's in regard to top sources? 3. Do top results from alternative search engines differ from Google's in regard to source concentration, i.e., are results distributed over more or fewer sources in different search engines? To answer these questions, we selected three alternative search engines to compare to Google. Aside from Bing, which is the biggest competitor to Google, we chose DuckDuckGo and Metager. DuckDuckGo uses results from Bing, Yahoo, and Yandex, and has the added benefit of advanced privacy settings such as non-personalized results. Metager, a German meta-search engine, aggregates results from several search engines, including Bing and Yandex. As such, either search engine might provide a more varied set of sources. Since the alternative search engines each have an index different from Google's, the comparison will give insights into what domains are favored by Google and which domains are excluded. Also, we will gain insights into the differences in ranking between them. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Different Search Engines Display and Prioritize Information

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References LITERATURE REVIEW Search result overlap By the mid-1990s, researching the overlap of search results between different search engines sparked interest for the purpose of estimating the size of the Web (Bharat, 1998; Ding & Marchionini, 1996; Lawrence & Giles, 1999). The generally small degrees of overlap indicated diverse underlying databases, each of limited size. Therefore, metasearch engines that combined the results of several search engines were meant to provide an additional value (Chignell et al., 1999) and attracted further research (i.e., Meng et al., 2002). Consequently, Spink et al. (Spink et al., 2006) included the meta search engine Dogpile in their extensive study on the overlap of search results. For a collection of 12,570 queries, the results found on the first search engine result page (SERP) of Ask Jeeves, Google, MSN Search, and Yahoo were captured. 84.9% of the results were unique to one search engine, while only 1.1% were shared by all search engines. Additionally, the low overlap between search engines also manifests in the ranking since only 7% of the top results were similar. This is consistent with previous studies that reported low overlap in ranking (Bar-Ilan, 2005; Bar‐Ilan et al., 2006) Subsequent studies report an increase of overlap in search results, while ranking algorithms appear to be the leading cause for differences in the presentation of the results. Bilal and Ellis (2011) compared major web search engines (Google, Bing, Yahoo) with search engines specifically designed for children (Ask Kids, Yahoo Kids) for queries of various lengths. Bing and Yahoo shared the same overlap with Google for nearly all queries, ranging between 22% and 40%. In contrast, Yahoo Kids had mostly unique results. However, the disparate relevance ranking between Bing and Yahoo suggests different ranking algorithms. Cardoso and Magalhães (2011) measured the overlap of search result rankings based on URLs and website contents. Search results for 40,000 queries were retrieved from Google and Bing. The overlap in domains was about 29%, and Google had more exclusive domains. When looking at the result sets without considering the positions, the similarity between the result sets increased. This indicates that Google and Bing have different ranking preferences but index mostly the same sources. Similarly, Agrawal et al. (2016) based their overlap analysis on content but factored in the description of the result snippets. The top 10 results of 67 informational queries were collected from Google and Bing. The results indicate a high overlap between Google and Bing, with a slightly higher similarity among the top 5 results. Most recently, a study about information on Covid-19 was conducted by Makhortykh et al. (2020). The first 50 results for queries in different languages were collected from Baidu, Bing, DuckDuckGo, Google, Yandex, and Yahoo. DuckDuckGo and Yahoo shared nearly 50% of their results, whereas the other pairs remained under 25%. Google had an overlap of around 10% with Bing and a negligible one with DuckDuckGo, Yandex, Yahoo, and Baidu. Source diversity Various studies took a closer look at search results and evaluated the domains, the types of sources, as well as their diversity across result sets and search engines. Comparing Google, Live Search, and Yahoo, Thelwall (2008) reported that Yahoo returned the highest amount of different domains to a query and also returned around 10% more top-level domains than other search engines. Höchstötter & Lewandowski (2009) showed that while the sources in top results of various search engines differ, there is a concentration on some popular sources in top results. More recently, Lewandowski & Sünkler (2019) collected search results on queries related to insurance providers and identified the distribution of top-level domains. Only ten different domains were found in the first result across all queries. The five most popular domains in the top 5 results are price comparison websites, which make up 88.4% of all first results. The share of popular domains decreases according to the ranking but remains at 42.9% at position ten. Unkel and Haim (2019) observed Google search results prior to the German parliamentary elections in 2017. The study found that result lists for candidates and parties exhibit a high share of self-administered websites. In contrast, results about election facts, guidance, and issues are mainly from general interest news, government information, and privately run websites. Among the top ten domains across all search results are seven news websites which account for a quarter of all search results. By contrast, Wikipedia made up 5.4% of all search results. The high share of news and Wikipedia articles is confirmed by Steiner et al. (2022) who compared the first results for queries on debated topics in Germany across the search engines Ask, Bing, DuckDuckGo, Google, and Ixquick. All search engines positioned Wikipedia in the first rank for queries on climate change and the Transatlantic Trade and Investment Partnership (TTIP); Ask did so for almost every query. In the other search engines, news sites were placed in the first rank most of the time. However, for the topic Covid-19, the sources are more diverse. As Makhortykh et al. (2020) observed, only Yahoo incorporates recent information from legacy media, whereas Bing strongly relied on healthcare-related sources and Google highlighted government-related websites. Yandex was the only search engine that included alternative media in its top 20 search results. Especially for recent topics that lack an established knowledge basis, the choice of search engine may considerably impact what information a user gets to see. This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References LITERATURE REVIEW Search result overlap By the mid-1990s, researching the overlap of search results between different search engines sparked interest for the purpose of estimating the size of the Web (Bharat, 1998; Ding & Marchionini, 1996; Lawrence & Giles, 1999). The generally small degrees of overlap indicated diverse underlying databases, each of limited size. Therefore, metasearch engines that combined the results of several search engines were meant to provide an additional value (Chignell et al., 1999) and attracted further research (i.e., Meng et al., 2002). Consequently, Spink et al. (Spink et al., 2006) included the meta search engine Dogpile in their extensive study on the overlap of search results. For a collection of 12,570 queries, the results found on the first search engine result page (SERP) of Ask Jeeves, Google, MSN Search, and Yahoo were captured. 84.9% of the results were unique to one search engine, while only 1.1% were shared by all search engines. Additionally, the low overlap between search engines also manifests in the ranking since only 7% of the top results were similar. This is consistent with previous studies that reported low overlap in ranking (Bar-Ilan, 2005; Bar‐Ilan et al., 2006) Subsequent studies report an increase of overlap in search results, while ranking algorithms appear to be the leading cause for differences in the presentation of the results. Bilal and Ellis (2011) compared major web search engines (Google, Bing, Yahoo) with search engines specifically designed for children (Ask Kids, Yahoo Kids) for queries of various lengths. Bing and Yahoo shared the same overlap with Google for nearly all queries, ranging between 22% and 40%. In contrast, Yahoo Kids had mostly unique results. However, the disparate relevance ranking between Bing and Yahoo suggests different ranking algorithms. Cardoso and Magalhães (2011) measured the overlap of search result rankings based on URLs and website contents. Search results for 40,000 queries were retrieved from Google and Bing. The overlap in domains was about 29%, and Google had more exclusive domains. When looking at the result sets without considering the positions, the similarity between the result sets increased. This indicates that Google and Bing have different ranking preferences but index mostly the same sources. Similarly, Agrawal et al. (2016) based their overlap analysis on content but factored in the description of the result snippets. The top 10 results of 67 informational queries were collected from Google and Bing. The results indicate a high overlap between Google and Bing, with a slightly higher similarity among the top 5 results. Most recently, a study about information on Covid-19 was conducted by Makhortykh et al. (2020). The first 50 results for queries in different languages were collected from Baidu, Bing, DuckDuckGo, Google, Yandex, and Yahoo. DuckDuckGo and Yahoo shared nearly 50% of their results, whereas the other pairs remained under 25%. Google had an overlap of around 10% with Bing and a negligible one with DuckDuckGo, Yandex, Yahoo, and Baidu. Source diversity Various studies took a closer look at search results and evaluated the domains, the types of sources, as well as their diversity across result sets and search engines. Comparing Google, Live Search, and Yahoo, Thelwall (2008) reported that Yahoo returned the highest amount of different domains to a query and also returned around 10% more top-level domains than other search engines. Höchstötter & Lewandowski (2009) showed that while the sources in top results of various search engines differ, there is a concentration on some popular sources in top results. More recently, Lewandowski & Sünkler (2019) collected search results on queries related to insurance providers and identified the distribution of top-level domains. Only ten different domains were found in the first result across all queries. The five most popular domains in the top 5 results are price comparison websites, which make up 88.4% of all first results. The share of popular domains decreases according to the ranking but remains at 42.9% at position ten. Unkel and Haim (2019) observed Google search results prior to the German parliamentary elections in 2017. The study found that result lists for candidates and parties exhibit a high share of self-administered websites. In contrast, results about election facts, guidance, and issues are mainly from general interest news, government information, and privately run websites. Among the top ten domains across all search results are seven news websites which account for a quarter of all search results. By contrast, Wikipedia made up 5.4% of all search results. The high share of news and Wikipedia articles is confirmed by Steiner et al. (2022) who compared the first results for queries on debated topics in Germany across the search engines Ask, Bing, DuckDuckGo, Google, and Ixquick. All search engines positioned Wikipedia in the first rank for queries on climate change and the Transatlantic Trade and Investment Partnership (TTIP); Ask did so for almost every query. In the other search engines, news sites were placed in the first rank most of the time. However, for the topic Covid-19, the sources are more diverse. As Makhortykh et al. (2020) observed, only Yahoo incorporates recent information from legacy media, whereas Bing strongly relied on healthcare-related sources and Google highlighted government-related websites. Yandex was the only search engine that included alternative media in its top 20 search results. Especially for recent topics that lack an established knowledge basis, the choice of search engine may considerably impact what information a user gets to see. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Examining Source Diversity in Web Search: A Comparative Study of Top Search Engines

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References ABSTRACT When it comes to search engines, users generally prefer Google. Our study aims to find the differences between the results found in Google compared to other search engines. We compared the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US. Google displays more unique domains in the top results than its competitors. Wikipedia and news websites are the most popular sources overall. With some top sources dominating search results, the distribution of domains is also consistent across all search engines. The overlap between Google and Bing is always under 32%, while Metager has a higher overlap with Bing than DuckDuckGo, going up to 78%. This study shows that the use of another search engine, especially in addition to Google, provides a wider variety in sources and might lead the user to find new perspectives. KEYWORDS Web search; search engine; web scraping; Google; source comparison INTRODUCTION Why should there be more than one search engine? While users may prefer one search engine over others for its usability, specialized features, or a more convenient integration into their technical environment, the question that interests us in this research is whether a user will benefit from using another search engine than Google when it comes to finding results from different sources. Our starting point is the fact that Google is the most-used search engine by far (StatCounter, 2022), that user to a large degree trust search engines to provide them relevant and useful results (European Commission, 2016; Purcell et al., 2012), and that only some users use another search engine in addition to Google (Schultheiß & Lewandowski, 2021). Users place great trust in search engines. This is reflected by the 91% of US users who said they find what they are looking for always or most of the time, and the 66% who believe search engines are a fair and unbiased source of information (Purcell et al., 2012). Furthermore, 78% of European internet and online platform users said they trust that their search engine results are the most relevant results (European Commission, 2016). Globally, users trust search engines more than any other source (including traditional news outlets) when it comes to news (Edelman Trust Institute, 2022) and users trust news found via search significantly more than news found on social media (Newman et al., 2021). As the Web is enormous and different search engines might prefer different sources, it is interesting to see whether the top sources shown in search results differ from one search engine to the other. It might be that an alternative search engine prefers results from "alternative" sources, e.g., in terms of political leaning or preferring noncommercial content providers. This all  comes down to whether alternative search engines are actually alternatives in regards to the results they display. If they were, possible benefits of using a search engine other than Google include finding different results, finding additional results, and finding more relevant results. No matter which of these goals a user aims to achieve, they will need other results than Google's. Therefore, it is interesting to see whether other search engines provide users with such results. There has been an ongoing discussion on alternative search engines and how Google's dominance in the search engine market can be broken. Approaches range from establishing single alternative search engines to building infrastructures for such alternatives (e.g., Lewandowski, 2019); also see Mager, 2014). With Google dominating the search engine market (StatCounter, 2022), it often seems that there are no alternatives at all. On the other hand, the number of alternative (or simply "other") search engines is often overestimated. Many seem-to-be search engines are merely search portals displaying results from a partner instead of generating the results from their own index. For instance, Yahoo and Ecosia get their results from Bing and can therefore not be considered search engines in their own right. But still, there may be other reasons for using a search engine without its own index. Some of the unique benefits alternative search engines advertise are privacy (e.g., Startpage and DuckDuckGo) or being a company investing its profits in environmental projects (e.g., Ecosia). Another type of search engine is the meta search engine (e.g., Metager). Such an engine sends the queries to several other search engines, then aggregates and re-ranks the top results. We deem it especially interesting whether such an approach will lead to a wider variety of search results, i.e., results from a more diverse set of sources. So, in the context of our research, we will consider any search engine that either has its own index or provides a unique selection and re-ranking of results from one or more indexes as an alternative search engine. We are especially interested in the differences in the source distribution; the relevance of the results is out of the scope of our research. More than 20 years ago, Introna & Nissenbaum (2000) argued that search engines as commercial operations tend to prefer big websites and, therefore, a portion of the Web, i.e., the smaller sites, remain hidden from view. Studies measuring what users select seem to confirm this: Goel et al. (2010) found that within Yahoo, only 10,000 websites account for approximately 80% of result clicks. It is important to note that this does not merely result from user preference for particular sources but that users predominantly select from the top results shown by the search engine. What is out of the immediate view of users will not be chosen (Lewandowski & Kammerer, 2021). It is striking that few studies have compared the results between different search engines in recent years. Older studies (see literature review section) overall found that top results from different search engines did not overlap too much. In this paper, we address how the top results of Google differ from alternatives and, therefore, whether it is worthwhile for a user to consider these  alternatives. If a search engine other than Google produced very similar results to Google, a user would not benefit much from using that search engine when source variety is considered. This paper is available on arxiv under CC 4.0 license.
Lead image by Justin Morgan on Unsplash Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References ABSTRACT When it comes to search engines, users generally prefer Google. Our study aims to find the differences between the results found in Google compared to other search engines. We compared the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US. Google displays more unique domains in the top results than its competitors. Wikipedia and news websites are the most popular sources overall. With some top sources dominating search results, the distribution of domains is also consistent across all search engines. The overlap between Google and Bing is always under 32%, while Metager has a higher overlap with Bing than DuckDuckGo, going up to 78%. This study shows that the use of another search engine, especially in addition to Google, provides a wider variety in sources and might lead the user to find new perspectives. KEYWORDS Web search; search engine; web scraping; Google; source comparison INTRODUCTION Why should there be more than one search engine? While users may prefer one search engine over others for its usability, specialized features, or a more convenient integration into their technical environment, the question that interests us in this research is whether a user will benefit from using another search engine than Google when it comes to finding results from different sources. Our starting point is the fact that Google is the most-used search engine by far (StatCounter, 2022), that user to a large degree trust search engines to provide them relevant and useful results (European Commission, 2016; Purcell et al., 2012), and that only some users use another search engine in addition to Google (Schultheiß & Lewandowski, 2021). Users place great trust in search engines. This is reflected by the 91% of US users who said they find what they are looking for always or most of the time, and the 66% who believe search engines are a fair and unbiased source of information (Purcell et al., 2012). Furthermore, 78% of European internet and online platform users said they trust that their search engine results are the most relevant results (European Commission, 2016). Globally, users trust search engines more than any other source (including traditional news outlets) when it comes to news (Edelman Trust Institute, 2022) and users trust news found via search significantly more than news found on social media (Newman et al., 2021). As the Web is enormous and different search engines might prefer different sources, it is interesting to see whether the top sources shown in search results differ from one search engine to the other. It might be that an alternative search engine prefers results from "alternative" sources, e.g., in terms of political leaning or preferring noncommercial content providers. This all  comes down to whether alternative search engines are actually alternatives in regards to the results they display. If they were, possible benefits of using a search engine other than Google include finding different results, finding additional results, and finding more relevant results. No matter which of these goals a user aims to achieve, they will need other results than Google's. Therefore, it is interesting to see whether other search engines provide users with such results. There has been an ongoing discussion on alternative search engines and how Google's dominance in the search engine market can be broken. Approaches range from establishing single alternative search engines to building infrastructures for such alternatives (e.g., Lewandowski, 2019); also see Mager, 2014). With Google dominating the search engine market (StatCounter, 2022), it often seems that there are no alternatives at all. On the other hand, the number of alternative (or simply "other") search engines is often overestimated. Many seem-to-be search engines are merely search portals displaying results from a partner instead of generating the results from their own index. For instance, Yahoo and Ecosia get their results from Bing and can therefore not be considered search engines in their own right. But still, there may be other reasons for using a search engine without its own index. Some of the unique benefits alternative search engines advertise are privacy (e.g., Startpage and DuckDuckGo) or being a company investing its profits in environmental projects (e.g., Ecosia). Another type of search engine is the meta search engine (e.g., Metager). Such an engine sends the queries to several other search engines, then aggregates and re-ranks the top results. We deem it especially interesting whether such an approach will lead to a wider variety of search results, i.e., results from a more diverse set of sources. So, in the context of our research, we will consider any search engine that either has its own index or provides a unique selection and re-ranking of results from one or more indexes as an alternative search engine. We are especially interested in the differences in the source distribution; the relevance of the results is out of the scope of our research. More than 20 years ago, Introna & Nissenbaum (2000) argued that search engines as commercial operations tend to prefer big websites and, therefore, a portion of the Web, i.e., the smaller sites, remain hidden from view. Studies measuring what users select seem to confirm this: Goel et al. (2010) found that within Yahoo, only 10,000 websites account for approximately 80% of result clicks. It is important to note that this does not merely result from user preference for particular sources but that users predominantly select from the top results shown by the search engine. What is out of the immediate view of users will not be chosen (Lewandowski & Kammerer, 2021). It is striking that few studies have compared the results between different search engines in recent years. Older studies (see literature review section) overall found that top results from different search engines did not overlap too much. In this paper, we address how the top results of Google differ from alternatives and, therefore, whether it is worthwhile for a user to consider these  alternatives. If a search engine other than Google produced very similar results to Google, a user would not benefit much from using that search engine when source variety is considered. This paper is available on arxiv under CC 4.0 license. Lead image by Justin Morgan on Unsplash This paper is available on arxiv under CC 4.0 license. available on arxiv Lead image by Justin Morgan on Unsplash Justin Morgan on Unsplash

A Comparison of Source Distribution and Result Overlap in Web Search Engines

ওয়েব সার্চ ইঞ্জিনে সোর্স ডিস্ট্রিবিউশন এবং ফলাফল ওভারল্যাপের তুলনা

Ein Vergleich der Quellenverteilung und der Ergebnisüberschneidung bei Websuchmaschinen

Una comparación de la distribución de fuentes y la superposición de resultados en los motores de búsqueda web

Une comparaison de la distribution des sources et du chevauchement des résultats dans les moteurs de recherche Web

वेब सर्च इंजन में स्रोत वितरण और परिणाम ओवरलैप की तुलना

ウェブ検索エンジンにおけるソース分布と結果重複の比較

웹 검색엔진의 소스 분포와 결과 중첩 비교

Uma comparação entre distribuição de fontes e sobreposição de resultados em mecanismos de pesquisa na web

Сравнение распределения источников и перекрытия результатов в поисковых системах веб-сайтов

Web Arama Motorlarında Kaynak Dağılımı ve Sonuç Örtüşmesinin Karşılaştırılması

So sánh sự phân phối nguồn và sự chồng chéo kết quả trong các công cụ tìm kiếm trên web

网络搜索引擎中源分布和结果重叠的比较

This paper is available on Arxiv under CC 4.0 license. Authors: (1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern; (2) Aleksandra Urman, Social Computing Group, University of Zurich; (3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. Table of Links Author Note Abstract Theoretical background Methodology Findings Conclusion and References Methodology Data collection To investigate how COVID-19 is framed visually via search engines, we collected data from five search engines: Bing, DuckDuckGo, Google, Yandex, and Yahoo. We selected these search engines because they are the ones with the largest share of the international search market (Statcounter 2020) or dominate their major local markets (e.g., Yandex in the case of Russia). To collect the data, we used a novel algorithmic auditing approach based on large-scale simulation of user browsing behavior via virtual agents (Ulloa, Makhortykh and Urman 2022). Virtual agents are software programs which can mimic user behavior (e.g., by entering URLs in the browser). Unlike earlier studies (e.g., Unkel and Haim 2019; Hannak et al. 2013) that rely on crowdsourced user data or small-scale simulations of browser behavior, our approach allows scaling the analysis to make it more robust and also permits conducting it in a controlled environment. The latter feature allowed us to investigate how image search results related to COVID-19 are filtered and ranked under a default - i.e., non-personalized - conditions. The data were collected on February 26, 2020, two weeks before the World Health Organization declared COVID-19 a pandemic. We deployed 83 machines from the Amazon Web Services Frankfurt cluster with each machine hosting two virtual agents (one on Firefox and another on Chrome). The agents imitated three sessions of user browsing behavior and were evenly distributed among the search engines, so each search engine was queried by 33/34 agents. All agents started each session simultaneously and used image search for the term “coronavirus” in English (session 1), Russian (session 2) and Mandarin Chinese (session 3; simplified characters were used). Then, the agents navigated through the result page(s) to retrieve links to the first 50 images from HTML. Following each session, the browsers were cleaned to prevent previous searches from affecting the latter queries. We removed the data that can be accessed by the search engine’s JavaScript (e.g., cookies) and the data accessible by the browser (e.g., browsing history). Data analysis To analyze collected data, we used qualitative framing analysis based on the generic frame typology developed by Semetko and Valkenburg (2000). The typology includes five types of frames: 1) conflict: frames emphasizing conflict between individuals, groups, or institutions; 2) human interest: frames bringing a human face or an emotional angle to the presentation of an issue/problem; 3) economic consequences: frames reporting an event or problem in terms of its economic consequences; 4) morality: frames putting the event or issue in the context of religious tenets or moral prescriptions; 5) responsibility: frames presenting an issue in such a way as to attribute responsibility for its cause/solution to either the government or to an individual/group. Similar to Kee, Faridah and Normah (2010), we adopted a set of 20 questions used by Semetko and Valkenburg (2000) to measure the strength of the frames for each individual image we analyzed. Each question could be responded with either “yes” or “no”; the responses were used to measure the strength of the respective frame. The frame strength was calculated by summing the responses to the related questions (e.g., five questions for the human interest frame and the three questions for the economic one) which were quantified proportionally (e.g., each “yes” answer for one of the five human interest questions added 0.2 to the strength of the human interest frame for the respective image as it contained five questions, whereas for the economic frame questions each “yes” answer contributed 0.33 proportional to three questions). For the analysis, we looked at the 50 most frequent images among the search results acquired by the agents querying the same search engine. These images were coded by the three authors of the chapter. The coding was then checked for consistency by one of the coders and the disagreements between the coders were discussed and consensus-coded. This paper is available on under a CC 4.0 license. Arxiv

The Visual Framing of COVID-19 on Search Engines

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern;
(2) Aleksandra Urman, Social Computing Group, University of Zurich;
(3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. Table of Links Author Note Abstract Theoretical background Methodology Findings Conclusion and References Abstract In today’s high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term “coronavirus” in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines. Keywords: search engine, algorithm audit, COVID, framing, news frames, Google, image search, visual communication In today’s “high-choice” (Van Aelst et al. 2017) media environment, search engines such as Google play an integral role in informing individuals about important societal developments. Together with social media and news aggregators, search engines increasingly become a major pathway to finding and engaging with the latest news (Newman et al. 2019) and historical information used to contextualize these recent developments (Zavadski and Toepfl 2019; Makhortykh, Urman and Ulloa 2021). Using complex algorithms, search engines filter and rank sources to counter the information overload and supply their users with the most relevant content. By prioritizing certain sources and judging how relevant a particular piece of information is, search engines serve as informationgatekeepers (Laidlaw 2010) that structure individual and collective knowledge about the subjects varying from elections (Trielli and Diakopoulos 2022) to diseases (Paramita et al. 2021) to the matters of gender and race (Urman, Makhortykh and Ulloa 2022). The gatekeeping functions of search engines translate into their substantial influence on different aspects of contemporary societies, in particular the ones related to information literacy (van Dijk 2010). This influence varies from search engines’ information ranking affecting undecided voter preferences (Epstein and Robertson 2015) to their algorithmically constructed knowledge hierarchies reinforcing gender and racial stereotypes (Noble 2018; Urman and Makhortykh 2022). The functionality of search engines becomes particularly important at times of crises, when the need to create new hierarchies of knowledge in the face of uncertainty often leads to a state of “epistemic instability” (Harambam 2020) where the authoritative sources of truth are challenged, and alternative interpretations thrive. Under such conditions, filtering and ranking mechanisms used by search engines play an integral role in determining how individuals and societies understand the causes and the consequences of the current situation and decide on their course of action. In this chapter, we look at how the above-mentioned complexities of information distribution via search engine algorithms affect pandemic literacy during the COVID-19 crisis. Understanding pandemic literacy as the ability to locate and effectively use information related to health threats associated with widespread epidemic diseases, we examine how search engines can facilitate, but also impede the ability of individuals and societies to stay informed about the COVID-19 pandemic. Specifically, we investigate the visual aspect of pandemic literacy by looking at visual representations of COVID-19 constructed by five major search engines in February 2020. Our interest in COVID-19 is characterized by the profound epistemic crisis experienced in relation to the pandemic and amplified by the intense use of digital media for disseminating false information about the disease (Allem 2020). By scrutinizing how search engines represent the pandemic, we strive to advance the understanding of their role at the time of epistemic instability and how search engines construct knowledge hierarchies in relation to a new and highly contradictory phenomenon. Our decision to focus on the visual representation of COVID-19 by search engines as contrasted to its textual representation that was examined in earlier studies (e.g. Makhortykh, Urman, and Ulloa 2020; Toepfl, Kravets, Ryzhova and Beseler 2022) is attributed to images as an effective means of communicating complex phenomena, in particular those that are hard to express verbally (Bleiker 2018). Images also stir strong emotional responses that make them a potent catalyst of societal mobilization but also result in their frequent abuse for manipulating public opinion (Ruijgrok 2017). To investigate how search engines represent and interpret COVID-19 visually, we combined qualitative analysis of visual frames—i.e., consistent patterns of selecting some aspects of the perceived reality and making them more salient (Entman 1993)—with quantitative techniques of algorithmic auditing used to investigate the functionality and impact of “decision-making algorithms” (Mittelstadt 2016). Utilizing a novel method of algorithmic auditing to extract visual search results, we compared the use of generic news frames (e.g., the attribution of responsibility, human interest, and economics; Semetko and Valkenburg 2000) for framing COVID-19 on five search engines: Google, Bing, Yahoo, Yandex and DuckDuckGo in English, Russian, and Mandarin Chinese. While doing so, we specifically investigated the following three research questions: How different/similar is the representation of COVID-19 via search engines compared with framing of earlier pandemics in legacy media? What types of news frames were prevalent in the representation and interpretation of COVID- 19? And are there substantial differences in the representation of COVID-19 among different search engines and languages? This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern; (2) Aleksandra Urman, Social Computing Group, University of Zurich; (3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern; (2) Aleksandra Urman, Social Computing Group, University of Zurich; (3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. Table of Links Author Note Author Note Abstract Abstract Theoretical background Theoretical background Methodology Methodology Findings Findings Conclusion and References Conclusion and References Abstract In today’s high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term “coronavirus” in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines. Keywords: search engine, algorithm audit, COVID, framing, news frames, Google, image search, visual communication In today’s “high-choice” (Van Aelst et al. 2017) media environment, search engines such as Google play an integral role in informing individuals about important societal developments. Together with social media and news aggregators, search engines increasingly become a major pathway to finding and engaging with the latest news (Newman et al. 2019) and historical information used to contextualize these recent developments (Zavadski and Toepfl 2019; Makhortykh, Urman and Ulloa 2021). Using complex algorithms, search engines filter and rank sources to counter the information overload and supply their users with the most relevant content. By prioritizing certain sources and judging how relevant a particular piece of information is, search engines serve as informationgatekeepers (Laidlaw 2010) that structure individual and collective knowledge about the subjects varying from elections (Trielli and Diakopoulos 2022) to diseases (Paramita et al. 2021) to the matters of gender and race (Urman, Makhortykh and Ulloa 2022). The gatekeeping functions of search engines translate into their substantial influence on different aspects of contemporary societies, in particular the ones related to information literacy (van Dijk 2010). This influence varies from search engines’ information ranking affecting undecided voter preferences (Epstein and Robertson 2015) to their algorithmically constructed knowledge hierarchies reinforcing gender and racial stereotypes (Noble 2018; Urman and Makhortykh 2022). The functionality of search engines becomes particularly important at times of crises, when the need to create new hierarchies of knowledge in the face of uncertainty often leads to a state of “epistemic instability” (Harambam 2020) where the authoritative sources of truth are challenged, and alternative interpretations thrive. Under such conditions, filtering and ranking mechanisms used by search engines play an integral role in determining how individuals and societies understand the causes and the consequences of the current situation and decide on their course of action. In this chapter, we look at how the above-mentioned complexities of information distribution via search engine algorithms affect pandemic literacy during the COVID-19 crisis. Understanding pandemic literacy as the ability to locate and effectively use information related to health threats associated with widespread epidemic diseases, we examine how search engines can facilitate, but also impede the ability of individuals and societies to stay informed about the COVID-19 pandemic. Specifically, we investigate the visual aspect of pandemic literacy by looking at visual representations of COVID-19 constructed by five major search engines in February 2020. Our interest in COVID-19 is characterized by the profound epistemic crisis experienced in relation to the pandemic and amplified by the intense use of digital media for disseminating false information about the disease (Allem 2020). By scrutinizing how search engines represent the pandemic, we strive to advance the understanding of their role at the time of epistemic instability and how search engines construct knowledge hierarchies in relation to a new and highly contradictory phenomenon. Our decision to focus on the visual representation of COVID-19 by search engines as contrasted to its textual representation that was examined in earlier studies (e.g. Makhortykh, Urman, and Ulloa 2020; Toepfl, Kravets, Ryzhova and Beseler 2022) is attributed to images as an effective means of communicating complex phenomena, in particular those that are hard to express verbally (Bleiker 2018). Images also stir strong emotional responses that make them a potent catalyst of societal mobilization but also result in their frequent abuse for manipulating public opinion (Ruijgrok 2017). To investigate how search engines represent and interpret COVID-19 visually, we combined qualitative analysis of visual frames—i.e., consistent patterns of selecting some aspects of the perceived reality and making them more salient (Entman 1993)—with quantitative techniques of algorithmic auditing used to investigate the functionality and impact of “decision-making algorithms” (Mittelstadt 2016). Utilizing a novel method of algorithmic auditing to extract visual search results, we compared the use of generic news frames (e.g., the attribution of responsibility, human interest, and economics; Semetko and Valkenburg 2000) for framing COVID-19 on five search engines: Google, Bing, Yahoo, Yandex and DuckDuckGo in English, Russian, and Mandarin Chinese. While doing so, we specifically investigated the following three research questions: How different/similar is the representation of COVID-19 via search engines compared with framing of earlier pandemics in legacy media? What types of news frames were prevalent in the representation and interpretation of COVID- 19? And are there substantial differences in the representation of COVID-19 among different search engines and languages? This paper is available on Arxiv under CC 4.0 license. Arxiv Arxiv

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