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Our escort had been directed to prepare our abode for the night at the inn

Hear you not the rushing sound of the coming tempest?

I have lingered thus long on the extreme bank

I returned to my family estate in the autumn of the year 2092

The stars still shone brightly when I awoke

Is there such a feeling as love at first sight?

With a beating heart and fearful, I knew not why

Happy, thrice happy, were the months, and weeks, and hours of that year

a SWOT analysis is an evaluation of the fundamental, operational, technical, social, economic, and even, to some degree, administrative elements of a project. This is not a model to be used for trading purposes. (NFA, DYOR) *Note: Composed of four elements, Strengths, Weaknesses, Opportunities, and Threats, a SWOT analysis framework provides excellent insight for establishing a high-level understanding of the state of a project's well-being through the lens of a birds-eye view. It can help formulate decisions around which areas require more attention, set performance goals, and organize a foundational understanding of where a project is headed. Rarely (if ever) used in crypto, it is time to apply this timeless method of evaluation to the digital asset space. Today, Stacks (STX), the layer 2 solution that is bringing smart contract capabilities to the Bitcoin network, will get a . SWOT 💪 Strengths (Internal) (Helpful) 1. PoX Consensus Model This is a very novel, creative solution for tithing the operational logic of the Stacks network to Bitcoin. Proof-of-transfer is a mechanism that utilizes transactional entropy as the underlying modality to mint new tokens. Credit must be given to Stacks for creating this system (which never existed prior), and it acts as a very abstract social pegging. Based on the use of assets from Proof-Of-Work-based chains, miners on the Bitcoin Network transfer their assets to Validators on Stacks (called Stackers) in return for the chance to stream the next batch of blocks and receive the newly minted STX tokens. 2. On-Chain TVL increasing The trendline of how much value is locked on Stacks in USD terms has been showing directionally improving. While it is possible that the trend benefits alongside a greater market recovery from the beginning of 2023, the fact there are already DEFI and NFT initiatives taking place on-chain signals a blue ocean of opportunity. 3. sBTC Attempting to bring TVL into its ecosystem in the form of Bitcoin and replicate the massive success of WBTC, sBTC promises to be the most trustless method of bridging BTC. Utilizing a two-way pegging system where users would deposit BTC into a nominated address that would then, in turn, be minted on Stacks at a 1:1 ratio and de-peg or move their assets back off of Stacks onto Bitcoin. What sets sBTC apart from all other pegging systems in the industry today is the architecture of capital flows. Bridging a BTC from Bitcoin to Stacks is permissionless and maximally frictionless; withdrawing requires some form of authority attestation. Stacks has devised a model for establishing the withdrawal authority in a maximally decentralized way. 4. Bridges to EVM chain Connecting the most prominent networks in terms of unlocks a new degree of capital, a new grade of developers, and a greater surface area for potentially marketing Stack's network in the smorgasbord of applications that live around the EVM ecosystem. At this time, Stacks is only bridged to Ethereum and Binance Smart Chain; however, introducing each new network should provide the project with some exciting technical roadmap developments over the next months/years. 5. Uses Bitcoin as Data Availability Layer In the new modular landscape where technologies are constantly introducing more complexity and more diversity in an attempt to cater to a still extremely young market, Stack’s focus on the Bitcoin ecosystem for data availability brings a novel level of decentralization and security to the dApp space. Data availability is arguably the most important part of any decentralized information system; given that Bitcoin is the most resilient (by raw computational power) and the most ridging, the data that Bitcoin's network governs can be thought of as being the most secure. This anchoring to Bitcoin allows STX to inherit some of its most desirable properties. 6. STX token was sold as an Approved Security Stacks token was the first to receive the regulatory green light to be sold to the public as a compliant digital asset. This makes STX an extremely interesting asset for potential Tradfi market participants. The sale took place in 2019, setting a precedent for any other projects in the space to follow in their footsteps. 7. STX Validator Incentives STX has one of the few, possibly only, models for the network participants (validators) to earn real yield through their involvement. Given that Stackers actually receive BTC that is used within its consensus model, there can be a real-world demand vector attributed to the STX token (even if only through second or third-degree relationships). 😞 Weaknesses (Internal) (Harmful) 1. BNS Abandoned Twice Stacks released BNS (Bitcoin Name Services) sometime in July of 2022. With over a year of observation, it seems that BNS does not really have that much of an important place in the digital asset market. Surely, it was released in the mother of all crypto bear markets, where approximately 300,000 BNS domains were sold. However, it has drawn a pattern of hype followed by absolute disregard. If the hype cooled down to a low of -95%, it would be feasible, but as the charts show us, the hype is followed by a fall to actual 0 in some cases. Moreover, all on-chain activity is to some degree faked by either project founders or supporters, or at the very least not Sybil conscious (a single entity can easily just own multiple BNS domains), likely arriving at a true number some odd 10x lower than the total amount sold. *This is by no means an existential crisis of any kind for Stacks, just some worthwhile food for thought in gauging ecosystemic resilience. 2. Bitcoin MEV Sharing a security model is known to be an obvious risk factor for MEV. Stacks was (and at this time still is) subject to the economic vulnerability present in the current version of PoX, where Bitcoin Miners can technically conduct fraudulent transactions without there being any risk to them; Stacks Network cannot punish Bitcoin Miners. Savvy miners came in and began only to include the actions related to their own nodes, pushing assets through a bottleneck that totally eliminated competition from other STX miners. 🧐 Opportunities (External) (Helpful) 1. Ultra Low Network DAU/DAA Both an opportunity and a threat. At the painfully modest level of roughly 3,000 Daily active users (not knowing how many of these are Sybils or bots), in the event the network gains a tiny portion of the interest that will flood the markets at some point, the potential external economic impact easily qualifies as enough to break previous ATH's. Moreover, considering how much quality infrastructure has actually been developed in the process, is STX attracts the developer community, then orders of magnitude growth await in terms of useability, adoption, and access. 2. No Real Competition Stacks has carved out a decently strong reputation for itself as THE project that is approaching Bitcoin programmability, scalability, and interoperability in the most crypto-pure way "decentralized." Positioned very uniquely against the few other participants (Liquid, Lighting Network, Rootstock), Stacks is the only one to provide the spectrum of benefits with minimal negative externalities. 3. Appealing to TradFi Stacks has carved out a niche that ticks the boxes for many traditional financial entities to be interested in participating in its ecosystem. The real yield of Bitcoin, the security of Bitcoin, a digital asset that was brought to market under regulatory supervision, scalable smart contract environment, and reputable findings with tremendous backing from prominent VCs. STX is easily crypto that likely every major enterprise will, at the very least, consider in their Web3 journies. 😳 Threats (External) (Harmful) 1. Graveyard of Bitcoin-based Solutions Rootstock, colored coins, Ordinals, and a handful of other attempts have been made to introduce more functionality on Bitcoin. Nearly all of them have failed to pick up steam, existing only within tiny niche communities until eventually dying off and becoming relics. If attempting to evaluate the long-term survivability of Stacks based on the niche it is operating in, the odds are stacked against them (pun intended). 2. Ultra Low Network DAU/DAA Both an opportunity and a threat. Ultra-low levels of adoption could be a direct indicator of the relative unimportance of the project or perhaps just a failure in marketing to users. Considering that at the absolute peak hype, the Stacks networks showed just about 9,000 daily active addresses. More likely than not, a decent portion of this can be attributed to bot activity, which dampens the interest levels even further. This extremely low level of engagement shows one of two things: either honesty from the network operators in showing activity or total disregard for the network, and the first inadvertently points to the second. Takeaway: Positive, unorthodox overall feeling about the project. Technology is there. Unique market position and produce offering is there. Marketing and social presence are definitely lagging, but out of all things, it is easiest to sort out. I'm not saying building communities is easy; I'm saying that building quality products is much more noble. Conclusion: Stacks is to Bitcoin what Polygon is to Ethereum. To be totally honest, I was an extreme skeptic of stacks. I actually considered it trash for a long while. But, after a thorough digging, I am humbled to admit that I was wrong; it's legit. While I now see the value proposition and understand why institutions might find this attractive, I would still NOT deposit my Bitcoin into sBTC and will most likely never use the smart contract network itself. Would I invest in the token itself (STX)? Yes. Small, controlled DCA with a better chance for higher returns than just holding Bitcoin. Under a different set of circumstances (where I had far more wealth), I would definitely explore becoming a validator on their network, but from my calculations, anything less than ~$500,000 is not worth the time. If you know something that I don't or feel as though I might have missed anything worth noting, please do share. I would tremendously appreciate some feedback. Thank you so much for reading, I hope this serves you well on your journey. Live long and prosper 🥂

The Stacks (STX) SWOT Analysis

Tell us a bit about yourself. For example, name, profession, and personal interests. Hi, my name is Marianna Alshina and I am an experienced startup founder, currently CBDO at Know Your Dosh. I’m really into research, stats, and numbers telling stories of people. There’s a whole world to explore and I’m on it. Interesting! What was your latest Hackernoon Top story about? My latest top story was about and I did an interview with Jane Fisher who shed light on this topic with some interesting insights! finding female co-founders Do you usually write on similar topics? If not, what do you usually write about? Although it’s not the main area of interest, exploring what it means to be a female founder today is certainly a part of my founder and personal journies. There are unique challenges for me as a person as well as challenges for a wider society to overcome. If I could add my 2 cents and provide an interesting insight or look at this from a different angle, I’d be totally proud of myself. Great! What is your usual writing routine like (if you have one?) Wake up at 5 am, make coffee… Just kidding! I am quite chaotic and inspiration-driven. Once I’m set on a topic of personal interest I can finish a draft in one go, then review it the next day and submit. Sometimes a piece is just way too big or requires weeks of research, for those I only have one word - commitment. Being a writer in tech can be a challenge. It’s not often our main role, but an addition to another one. What is the biggest challenge you have when it comes to writing? I tend to have tunnel vision in my main job, so I do enjoy the switch to writing and reflecting on my past experiences and the future of the industry. The challenging part is to find interesting perspectives to look at things. There are many smart and talented authors out there and sometimes I might read some mind-blowing research or take and feel like I should’ve been the one who came up with that idea. What is the next thing you hope to achieve in your career? Building another successful startup would be a dream come true. I have also started mentoring other people who build early-stage startups and would love to do more of it. Wow, that’s admirable. Now, something more casual: What is your guilty pleasure of choice? Haagen-Dazs ice cream every day when it goes on sale for a week. It’s a bucket-a-day thing. Do you have a non-tech-related hobby? If yes, what is it? I am diving deeper and deeper into and crafts and this rabbit hole is infinite. fiber arts What can the Hacker Noon community expect to read from you next? Something to do with AI, startup strategy, or female founders! What’s your opinion on HackerNoon as a platform for writers? It’s a great place with a big community and welcoming authors. I’d say it’s also a great place to start writing - put together the first article and send it, it’s very validating to see it published and getting the views. Being chosen to be a headliner story is the next level of validation though and gives more energy to keep going. Sure, you could probably start writing on Medium much easier, but to me content curation is important. Thanks for taking the time to join our “ ” series. It was a pleasure. Do you have any closing words? Meet the Writer Thank you for building this platform and I look forward to connecting more with the community.

Meet the Writer: HackerNoon's Contributor Marianna Alshina, Chief Business Development Officer 

David Copperfield: Chapter 32 - The Beginning Of A Long Journey

1811 Dictionary in the Vulgar Tongue, by Francis Grose is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here: [ ]. Section J LINK TO TABLE OF LINK Section J JABBER. To talk thick and fast, as great praters usually do, to chatter like a magpye; also to speak a foreign language. He jabbered to me in his damned outlandish parlez vous, but I could not understand him; he chattered to me in French, or some other foreign language, but I could not understand him. JACK. A farthing, a small bowl serving as the mark for bowlers. An instrument for pulling off boots. JACK ADAMS. A fool. Jack Adams's parish; Clerkenwell. JACK AT A PINCH, A poor hackney parson. JACK IN A BOX, A sharper, or cheat. A child in the mother's womb. JACK IN AN OFFICE, An insolent fellow in authority. JACK KETCH. The hangman; vide DERRICK and KETCH. JACK NASTY FACE. A sea term, signifying a common sailor. JACK OF LEGS. A tall long-legged man; also a giant, said to be buried in Weston church, near Baldock, in Hertfordshire, where there are two stones fourteen feet distant, said to be the head and feet stones of his grave. This giant, says Salmon, as fame goes, lived in a wood here, and was a great robber, but a generous one; for he plundered the rich to feed the poor: he frequently took bread for this purpose from the Baldock bakers, who catching him at an advantage, put out his eyes, and afterwards hanged him upon a knoll in Baldock field. At his death he made one request, which was, that he might have his bow and arrow put into his hand, and on shooting it off, where the arrow fell, they would bury him; which being granted, the arrow fell in Weston churchyard. Above seventy years ago, a very large thigh bone was taken out of the church chest, where it had lain many years for a show, and was sold by the clerk to Sir John Tradescant, who, it is said, put it among the rarities of Oxford. JACK PUDDING. The merry andrew, zany, or jester to a mountebank. JACK ROBINSON. Before one could say Jack Robinson; a saying to express a very short time, originating from a very volatile gentleman of that appellation, who would call on his neighbours, and be gone before his name could be announced. JACK SPRAT. A dwarf, or diminutive fellow. JACK TAR. A sailor. JACK WEIGHT. A fat man. JACK WHORE. A large masculine overgrown wench. JACKANAPES. An ape; a pert, ugly, little fellow. JACKED. Spavined. A jacked horse. JACKMEN. See JARKMEN. JACKEY. Gin. JACOB. A soft fellow. A fool. JACOB. A ladder: perhaps from Jacob's dream. CANT. Also the common name for a jay, jays being usually taught to say, Poor Jacob! a cup of sack for Jacob. JACOBITES. Sham or collar shirts. Also partizans for the Stuart family: from the name of the abdicated king, i.e. James or Jacobus. It is said by the whigs, that God changed Jacob's name to Israel, lest the descendants of that patriarch should be called Jacobites. JADE. A term of reproach to women. JAGUE. A ditch: perhaps from jakes. JAIL BIRDS. Prisoners. JAKES. A house of office, a cacatorium. JAMMED. Hanged. CANT. JANIZARIES. The mob, sometimes so called; also bailiffs, their setters, and followers. JAPANNED. Ordained. To be japanned; to enter into holy orders, to become a clergyman, to put on the black cloth: from the colour of the japan ware, which is black. JARK. A seal. JARKMEN. Those, who fabricate counterfeit passes, licences, and certificates for beggars. JARVIS. A hackney coachman. JASON'S FLEECE. A citizen cheated of his gold. JAW. Speech, discourse. Give us none of your jaw; let us have none of your discourse. A jaw-me-dead; a talkative fellow. Jaw work; a cry used in fairs by the sellers of nuts. JAZEY. A bob wig. IDEA POT. The knowledge box, the head. See KNOWLEDGE BOX. JEFFY. It will be done in a jeffy; it will be done in a short space of time, in an instant. JEHU. To drive jehu-like; to drive furiously: from a king of Israel of that name, who was a famous charioteer, and mentioned as such in the Bible. JEM. A gold ring. CANT. JEMMY FELLOW. A smart spruce fellow. JEMMY. A crow. This instrument is much used by housebreakers. Sometimes called Jemmy Rook. JENNY. An instrument for lifting up the grate or top of a show-glass, in order to rob it. CANT. JERRYCUMMUMBLE. To shake, towzle, or tumble about. JERRY SNEAK. A henpecked husband: from a celebrated character in one of Mr. Foote's plays, representing a man governed by his wife. JESSAMY. A smart jemmy fellow, a fopling. JESIUT. See TO BOX THE JESUIT. JESUITICAL. Sly, evasive, equivocal. A jesuitical answer; an equivocal answer. JET. A lawyer. Autem jet; a parson. JEW. An over-reaching dealer, or hard, sharp fellow; an extortioner: the brokers formerly behind St. Clement's church in the Strand were called Jews by their brethren the taylors. JEW. A tradesman who has no faith, i.e. will not give credit. JEW BAIL. Insufficient bail: commonly Jews, who for a sum of money will bail any action whatsoever, and justify, that is, swear to their sufficiency; but, when called on, are not to be found. JEW'S EYE. That's worth a Jew's eye; a pleasant or agreeable sight: a saying taken from Shakespeare. JIBBER THE KIBBER. A method of deceiving seamen, by fixing a candle and lanthorn round the neck of a horse, one of whose fore feet is tied up; this at night has the appearance of a ship's light. Ships bearing towards it, run on shore, and being wrecked, are plundered by the inhabitants. This diabolical device is, it is said, practised by the inhabitants of our western coasts. JIG. A trick. A pleasant jig; a witty arch trick. Also a lock or door. The feather-bed jig; copulation. JIGGER. A whipping-post. CANT. JILT. A tricking woman, who encourages the addresses of a man whom she means to deceive and abandon. JILTED. Rejected by a woman who has encouraged one's advances. JINGLE BOXES. Leathern jacks tipped with silver, and hung with bells, formerly in use among fuddle caps. CANT. JINGLE BRAINS. A wild, thoughtless, rattling fellow. JINGLERS. Horse cosers, frequenting country fairs. JUG. See DOUBLE JUG. JUGGLER'S BOX. The engine for burning culprits in the hand. CANT. JUKRUM. A licence. JUMBLEGUT LANE. A rough road or lane. JUMP. The jump, or dining-room jump; a species of robbery effected by ascending a ladder placed by a sham lamp-lighter, against the house intended to be robbed. It is so called, because, should the lamp-lighter be put to flight, the thief who ascended the ladder has no means of escaping but that of jumping down. JUMPERS. Persons who rob houses by getting in at the windows. Also a set of Methodists established in South Wales. JUNIPER LECTURE. A round scolding bout. JURY LEG. A wooden leg: allusion to a jury mast, which is a temporary substitute for a mast carried away by a storm, or any other accident. SEA PHRASE. JURY MAST. A JOURNIERE mast; i.e. a mast for the day or occasion. JUST-ASS. A punning appellation for a justice. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Grose, Francis. 2004. 1881 Dictionary in the Vulgar Tongue. https://www.gutenberg.org/cache/epub/5402/pg5402.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

1811 Dictionary in the Vulgar Tongue: Section J

: Let me tell you a few facts about mobile phones that may surprise you According to nearly 90% of mobile internet time is spent on apps eMarketer There are around 4.5 million apps available in the app store ( ) statista 30,000 new iOS apps are released in the Apple App Store every month ( ) 42Matters In 2019 consumers downloaded 204 billion apps (and it keeps going up – ) App Annie More than 20% of all mobile apps in the Apple App Store are game apps, that’s 2x the number of business apps ( ) Statista Roughly 37% of Millenials list data usage as a key factor when deciding if they like a mobile app ( ) MindSea 21% of millennials say they open their apps more than 50 times per day ( ) The Manifest Gen Z spends 71% of their digital media time using smartphone apps ( ) ComScore And no, I did not research and compile this list myself, you should thank the awesome team at for that. MindSea So, why am I showing you all these stats? Well, it’s because seeing as the amount of time people spend on apps nowadays has skyrocketed, the amount of apps on the App Store has hooked on and rocketed up with it. So it’s getting harder and in 2022. harder to find an actual good, working and free app The best free iOS apps to get (2022) I’ve got a list of all the App Store’s categories, and I’ll provide one app for each, the list is below. I’m going to try and steer everyone knows about and already has, and will try to find apps you may have never tried before! clear of the common apps 📖 Books 🩺 Medical 💼 Business 🎵 Music 🛠️ Developer Tools 🧭 Navigation 🌎 Education 📻 News 🍿 Entertainment 📷 Photo & Video 💰 Finance 📆 Productivity 🍽️ Food & Drink 🔍 Reference 🎮 Games 🧩 Safari Extensions (macOS only) 🖼️ Graphics & Design 🛍️ Shopping 🚲 Health & Fitness 💬 Social Networking 💺 Lifestyle ⚽ Sports 🎈 Kids 🏝️ Travel 📰 Magazines & Newspapers 🔦 Utilities ⛅ Weather 📖 Books – Apple Books https://apple.co/3vBrW3D Apple Books is the single destination for all the books you love, and the ones you’re about to. Browse the Book Store, keep track of what you’ve read and want to read, and listen to enthralling audiobooks. All in one place. It has a hidden free section which you can access by doing the following: 1. Tap the Book Store tab at the bottom of the app’s Home page, then tap Browse Sections.
2. Tap Special Offers & Free. Scroll down, and you’ll find all the free e-books Apple has on offer. There’s always a great selection, including fiction, nonfiction, science fiction, fantasy, juvenile fiction, and even cookbooks. 💼 Business – Trello https://apple.co/3HEVZK7 Manage any team project or task thanks to Trello’s boards, cards and checklists. Improve collaboration and productivity—join their community of 50 million registered users! Organize anything with anyone—for free! Trello is a visual tool for organizing your work and life. Join more than 35 million registered users to organize all your projects at work, at home, or anywhere in between. Whether you’re planning a website design project, vacation, or company off-site, Trello is infinitely customizable and flexible for your every need. ⚒️ Developer Tools – Mimo https://apple.co/3MkFrKV Mimo is an awesome app for anyone that wants to get into development. Mimo can be used to advance your career, build apps or websites, or even become a developer! With over 60 projects and daily bite-sized lessons, it’s the perfect app for beginners-intermediates that just want to learn a few minutes of coding whenever they’ve got some spare time thought-out their day. 🌍 Education – Kahoot! https://apple.co/3C8wcci Surprisingly, Kahoot is an app I only discovered quite recently. You can get started without even creating an account, and there are quizzes on everything from Marvel to NASA. You can play with two people on the same device, or share the game pin with your friends, so you can play remotely (you can even play from their website). 🍿 Entertainment – Mission to Mars AR https://apple.co/3trjTDT Although it’s officially placed in the Education category, I think it makes a great entertainment app. Using Mission to Mars, you can explore Mars with the power of AI, take an interactive look at Perseverance and other rovers, drive them around, and explore Mars in immersive 3D. It’s a really fun app to explore, whilst also learning about the Mission to Mars! 💰 Finance – Mint https://apple.co/3sDscNK Nowadays most browsers are preloaded with your credit card details, so when you go to checkout for something, you barely second thought as your browser auto-fills all your credit info. Whilst it does make life much easier, it also has many downsides, including losing track of your spending habits. Mint is a fresh and safe way to manage your money. You can set goals, get personalized insights, track your spending and manage your subscriptions. It brings together all your finances to help you reach your financial goals! – Unfortunately Mint is only available for people in the US & Canada, so for those of you that can’t get it, here’s an excellent Mint alternative for you: For people outside of the United States & Canada – Emma Money Management https://emma.to/nightwolf https://emma.to/nightwolf Emma is a personal finance and budgeting tool that helps you avoid overdrafts, cancel wasteful subscriptions, track expenses and save money, basically a UK version of mint. 😉 🍽️ Food & Drink – Mealime https://apple.co/3hynFFY Mealime is a simple way for busy singles, couples, and families to plan their meals and eat healthier. Their meal plans & recipes are highly customizable so you can easily personalize a plan that works with your unique tastes and lifestyle. They have over 5,000,000 people who have used their meal plans to eat healthier, reduce stress, lose weight, save money, and live happier, more productive lives. 🎮 Gaming – Everdale https://apple.co/3Ki59hz Everdale is a strategic village building game. Grow your village and lead your villagers in a lush utopian community in Everdale! Play with your friends to build a society upon wits and cooperation. Farm and garden. Collect and craft. Trade and sell. Harvest and reap the rewards! 🖼️ Graphics & Design – Behance https://apple.co/3Kdz9uV Behance is the perfect app for designers looking for inspiration. Search millions of projects across creative fields and industries, including Graphic Design, Photography, Illustration, Interaction Design, Motion Graphics, Architecture, Product Design, Fashion, Advertising, Fine Arts, Crafts, Game Design, Sound …and much more! 🚲 Health & Fitness – CARROT Fit https://apple.co/3pzAc0k CARROT is a sadistic AI construct with one simple goal: to transform your flabby carcass into a Grade A specimen of the human race. She will do whatever it takes – including threatening, inspiring, ridiculing, and bribing you – to make this happen. If you work out and slough off those extra pounds, CARROT will reward you with fabulous prizes like app upgrades, cat facts, and permission to watch your friend eat a bag of potato chips. 💺 Lifestyle – Notion https://apple.co/3pBne1Y Write, plan & get organized in one place. Customize Notion to work the way you do. Capture notes quickly and organize them however you want, no matter where you are. Notion remembers it all so you don’t have to. Managing any project — from work requests to a list of chores — is a juggling act. In Notion, you can get a full view of what needs to get done so no balls get dropped. Make simple checklists or detailed project boards to turn tasks into action. 🎈 Kids – Quick Maths Jr. https://apple.co/3vBSpye Loved by teachers… and kids! Quick Math Jr. is an award-winning app that covers the essential foundations of mathematics, including counting, addition and subtraction, place value, writing numbers and much more for children aged 3 – 7. 📰 Magazines & Newspapers Medium – https://apple.co/3CcgOM7 Medium is an open-access social publishing platform that features a varied range of stories, ideas, and opinions. Anyone may contribute, and popular themes on Medium include everything from mental health to social media, as well as world events and breaking news, as well as productivity hacks. I also write on Medium, although they are mostly reposts of the articles from . You can drop a follow over here: my blog https://medium.com/@night-wolf 🩺 Medical – MyChart https://apple.co/3IEIuM0 See your medications, test results, upcoming appointments, medical bills, price estimates, and more all in one place, even if you’ve been seen at multiple healthcare organizations. Share your medical record with anyone who needs it. Send a message, get an online diagnosis and treatment, talk face-to-face over video, or arrange to follow up with a doctor in person, depending on the level of care you need. Stay on top of everyone’s appointments and check in on family members who need extra help, all from your account. 🎵 Music – Shazam https://apple.co/3hC52RG Shazam is an app that is perfectly integrated into iPhone. You can add the shortcut to the control centre by going to your iPhone settings, and now whenever you hear a TikTok song play, you can finally confirm that yes. It’s still drivers license. 🧭 Navigation – What3Words https://apple.co/3Mg99Rz what3words is an easy way to identify precise locations. Every 3m square has been given a unique combination of three words: a what3words address. Now you can find, share and navigate to precise locations using three simple words. 📻 News – Feedly https://apple.co/3CejWa3 Feedly is a powerful feed reader that collects information from all over the internet in one location. You can use it to keep up with breaking news, blogs, social networking posts, RSS feeds, and other sources of information on any topic you’re interested in. It skips all the junk and spam and gives you exactly what you’re looking for! 📷 Photo & Video – Dream by WOMBO https://apple.co/3Kd5ZMj Create beautiful artwork using the power of AI! Enter a prompt, pick an art style and watch WOMBO Dream turn your Using the power of AI, create stunning artwork! Enter a prompt, choose an art style, and WOMBO Dream will transform your idea into an AI-powered painting in seconds. If you are not really sure what AI is, as it’s being thrown about quite a lot nowadays, check out . this article here 🗓️ Productivity – Asana https://apple.co/3IJSpji Asana is the easiest way to manage team projects and your individual tasks. From the small stuff to the big picture, Asana organizes work so you and your teams are clear on what to do, when to do it, and how to get it done. 🔍 Reference – Idioms and Slang Dictionary https://apple.co/3hyn0ob Get detailed definitions of tens of thousands of idioms used in the United States and around the world. Every day, you speak them, but do you know where they came from? Learn about the origins of the phrases. 🧩 Safari Extensions (macOS only) – Ghostery https://apple.co/3MgbnQV Ghostery for Safari allows you to browse quickly, easily, and safely. It protects your online privacy by preventing trackers from accessing your personal information and by blocking adverts from appearing on the websites you visit. It provides the tracking protection that consumers have come to expect while reducing the need for modification and upkeep. 🛍️ Shopping – Twig https://apple.co/3hynkDp In less than 30 seconds, you can sign up and list your products. Just take photos of your stuff to sell and you’ll receive an instant appraisal. Twig banking services offer a quick and convenient way to earn money from the comfort of your own home with only a few taps. 💬 Social Networking – Flickr https://apple.co/3sDKdLX Flickr is a photo-centric platform that allows professional photographers to showcase their work while also admiring the work of others. It devotes more attention to the art of photography than any other major social media platform. Consider it a professional photographer’s Instagram. ⚽ Sports – One Football https://apple.co/3tucAvh OneFootball is the only soccer app you need to follow all the Soccer: news, videos, fixtures, scores, stats, calendar, videos, transfers market of all the competitions around the world: Premier League, La Liga, Serie A, Bundesliga, MLS, Liga MLS, Copa Libertadores, Copa Sudamericana and more. 🏝️ Travel – Journi Blog https://apple.co/3pyGIo5 Journi is the simplest way to document your life using stylish timelines that include photographs, notes, maps, and more. Easily select which photographs are private and turn them into printed photo books in seconds! Use it offline, collaborate on timelines, and sync across all of your devices! With Journi, you can get more out of your images and recall all of your life’s moments. 🔦 Utilities – Pocket https://apple.co/3vxYN9C Stop sending yourself links. When you come across a long read, interview, think piece, even a recipe, save it to your personal Pocket library with the button in your browser or with the Pocket app. 🌤️ Weather – CARROT Weather https://apple.co/3vFiJHB CARROT Weather is a crazy-powerful (and privacy-conscious) weather app that delivers hilariously twisted forecasts. Choose one of five personalities for CARROT, from the straitlaced “professional” to the profanity-laden “overkill.” You’ll actually be looking forward to a torrential downpour just to see what your weather robot has in store for you! And there are and carefully selected apps you should definitely try out! 27 awesome What one app do you use on a day to day basis and ? Let me know below and maybe I’ll make an updated list at one point! ✌️ cannot be without This article was originally posted on my blog, . I recently started it, as I’ve always been interested in technology, I have a lot to learn and can’t wait to see where this road takes me! 😉 https://kilabyte.org/ A bit about me
👋 Hi, I'm Night Wolf, I'm 16
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🌱 I'm currently doing a full-stack course
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📸 @nightwolf.tech
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The Ultimate List of Free iOS Apps you Need to Get (2022)

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References REFERENCES [1] Toril Aalberg and Anders Todal Jenssen. 2017. Gender Stereotyping of Political Candidates. Nordicom Review 28, 1 (2017), 17–32. https://doi.org/10.1515/nor-2017-0198 [2] Pushkal Agarwal, Nishanth Sastry, and Edward Wood. 2019. Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK. Proceedings of the International AAAI Conference on Web and Social Media 13, 01. [3] Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal M Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Rashid Shora, Syeda Zainab Akbar, and Joyojeet Pal. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. [4] Nichole M. Bauer. 2015. Emotional, Sensitive, and Unfit for Office? Gender Stereotype Activation and Support Female Candidates. Political Psychology 36, 6 (2015), 691–708. [5] Aditi Bhowmick, Paul Novosad, Sam Asher, Elliott Ash, Bilal Siddiqi, Christoph Goessmann, Daniel Chen, and Tanaya Devi. 2021. In-Group Bias in the Indian Judiciary: Evidence from 5.5 Million Criminal Cases. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). 47. https://doi.org/10.1145/3460112.3471943 [6] Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. [7] Tessa M. Ditonto, Allison J. Hamilton, and David P. Redlawsk. 2014. Gender Stereotypes, Information Search, and Voting Behavior in Political Campaigns. Political Behavior 36, 2 (2014), 335–358. https://doi.org/10.1007/s11109-013-9232-6 [8] Kathleen Dolan and Timothy Lynch. 2014. It Takes a Survey: Understanding Gender Stereotypes, Abstract Attitudes, and Voting for Women Candidates. American Politics Research 42, 4 (2014), 656–576. https://doi.org/10.1177/1532673X13503034 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672.
https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [cs.CY] https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html. [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls. [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/. [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References REFERENCES [1] Toril Aalberg and Anders Todal Jenssen. 2017. Gender Stereotyping of Political Candidates. Nordicom Review 28, 1 (2017), 17–32. https://doi.org/10.1515/nor-2017-0198 [2] Pushkal Agarwal, Nishanth Sastry, and Edward Wood. 2019. Tweeting MPs: Digital Engagement between Citizens and Members of Parliament in the UK. Proceedings of the International AAAI Conference on Web and Social Media 13, 01. [3] Arshia Arya, Soham De, Dibyendu Mishra, Gazal Shekhawat, Ankur Sharma, Anmol Panda, Faisal M Lalani, Parantak Singh, Ramaravind Kommiya Mothilal, Rynaa Grover, Sachita Nishal, Saloni Dash, Shehla Rashid Shora, Syeda Zainab Akbar, and Joyojeet Pal. 2022. DISMISS: Database of Indian Social Media Influencers on Twitter. [4] Nichole M. Bauer. 2015. Emotional, Sensitive, and Unfit for Office? Gender Stereotype Activation and Support Female Candidates. Political Psychology 36, 6 (2015), 691–708. [5] Aditi Bhowmick, Paul Novosad, Sam Asher, Elliott Ash, Bilal Siddiqi, Christoph Goessmann, Daniel Chen, and Tanaya Devi. 2021. In-Group Bias in the Indian Judiciary: Evidence from 5.5 Million Criminal Cases. In Proceedings of the 4th ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS ’21). 47. https://doi.org/10.1145/3460112.3471943 https://doi.org/10.1145/3460112.3471943 [6] Jose Camacho-Collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa-Anke, Fangyu Liu, Eugenio Martínez-Cámara, et al. 2022. TweetNLP: Cutting-Edge Natural Language Processing for Social Media. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. [7] Tessa M. Ditonto, Allison J. Hamilton, and David P. Redlawsk. 2014. Gender Stereotypes, Information Search, and Voting Behavior in Political Campaigns. Political Behavior 36, 2 (2014), 335–358. https://doi.org/10.1007/s11109-013-9232-6 https://doi.org/10.1007/s11109-013-9232-6 [8] Kathleen Dolan and Timothy Lynch. 2014. It Takes a Survey: Understanding Gender Stereotypes, Abstract Attitudes, and Voting for Women Candidates. American Politics Research 42, 4 (2014), 656–576. https://doi.org/10.1177/1532673X13503034 https://doi.org/10.1177/1532673X13503034 [9] Heather Evans. 2016. Do Women Only Talk about ‘Female Issues’? Gender and
Issue Discussion on Twitter. Online Information Review 40, 5 (2016), 660–672. https://doi.org/10.1108/OIR-10-2015-0338 https://doi.org/10.1108/OIR-10-2015-0338 [10] Heather K. Evans and Jennifer Hayes Clark. 2016. You Tweet Like a Girl!’: How Female Candidates Campaign on Twitter. American Politics Research 44, 2 (2016), 326–352. https://doi.org/10.1177/1532673X15597747 https://doi.org/10.1177/1532673X15597747 [11] Peter A. Gloor, Joao Marcos, Patrick M. de Boer, Hauke Fuehres, Wei Lo, and Keiichi Nemoto. 2015. Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment. arXiv:1508.00055 [ cs.CY ] https://doi.org/10.48550/ARXIV.1508.00055 cs.CY https://doi.org/10.48550/ARXIV.1508.00055 [12] Leonie Huddy and Nayda Terkildsen. 1993. Gender Stereotypes and the Perception of Male and Female Candidates. American Journal of Political Science 37, 1 (1993), 119. https://doi.org/10.2307/2111526 https://doi.org/10.2307/2111526 [13] Berrin Beasley John H. Parmelee, Nataliya Roman and Stephynie C. Perkins. 2019. Gender and Generational Differences in Political Reporters’ Interactivity on Twitter. Journalism Studies 20, 2 (2019), 232–247. https://doi.org/10.1080/1461670X.2017.1364140 https://doi.org/10.1080/1461670X.2017.1364140 [14] Ashiqur R. KhudaBukhsh Kunal Khadilkar and Tom M. Mitchell. 2022. Gender Bias, Social Bias, and Representation in Bollywood and Hollywood. Patterns 3, 4 (2022), 100486. https://doi.org/10.1016/j.patter.2022.100486 https://doi.org/10.1016/j.patter.2022.100486 [15] MingYu (Ethen) Liu. 2023. Topic Modeling. https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html . https://ethen8181.github.io/machine-learning/clustering/topic_model/LDA.html [16] J. Nathan Matias, Sarah Szalavitz, and Ethan Zuckerman. 2017. FollowBias: Supporting Behavior Change toward Gender Equality by Networked Gatekeepers on Social Media (CSCW ’17). 1082–1095. https://doi.org/10.1145/2998181.2998287 https://doi.org/10.1145/2998181.2998287 [17] Lindsey Meeks. 2016. Gendered Styles, Gendered Differences: Candidates’ Use of Personalization and Interactivity on Twitter. Journal of Information Technology & Politics 13, 4 (2016), 295–310. https://doi.org/10.1080/19331681.2016.1160268 https://doi.org/10.1080/19331681.2016.1160268 [18] Armin Mertens, Franziska Pradel, Ayjeren Rozyjumayeva, and Jens Wäckerle. 2019. As the Tweet, so the Reply? Gender Bias in Digital Communication with Politicians. In Proceedings of the 10th ACM Conference on Web Science (WebSci ’19). 193–201. [19] Sebo P. 2021. Using genderize.io to infer the gender of first names: how to improve the accuracy of the inference. J Med Libr Assoc. (2021). genderize.io [20] Anmol Panda, A’ndre Gonawela, Sreangsu Acharyya, Dibyendu Mishra, Mugdha Mohapatra, Ramgopal Chandrasekaran, and Joyojeet Pal. 2020. NivaDuck - A Scalable Pipeline to Build a Database of Political Twitter Handles for India and the United States. In International Conference on Social Media and Society. 200–209. [21] John H. Parmelee. 2013. Political Journalists and Twitter: Influences on Norms and Practices. Journal of Media Practice 14, 4 (2013), 291–305. https://doi.org/10.1386/jmpr.14.4.291_1 https://doi.org/10.1386/jmpr.14.4.291_1 [22] Pallavi Prasad. 2020. One in Every Seven Tweets About Indian Women Politicians Is Abusive: Study. https://www.theswaddle.com/women-politicians-india-twitter-trolls . https://www.theswaddle.com/women-politicians-india-twitter-trolls [23] Gazal Shekhawat, Parantak Singh, Rynaa Grover, Saloni Dash, Himani Negi, Ankur Sharma, Somya Sagarika, and Joyojeet Pal. 2021. Twitter Use by Politicians Across States in India. [24] Carole Spary. 2007. Female Political Leadership in India. Commonwealth & Comparative Politics 45, 3 (2007), 253–277. https://doi.org/10.1080/14662040701516821 https://doi.org/10.1080/14662040701516821 [25] Genderize team. 2023. Genderize. https://genderize.io/ . https://genderize.io/ [26] Jesse Holcomb Usher, Nikki and Justin Littman. 2018. Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias. The International Journal of Press/Politics 23, 3 (2018), 324–344. https://doi.org/10.1177/1940161218781254 https://doi.org/10.1177/1940161218781254 [27] Jason Gainous Wagner, Kevin M. and Mirya R. Holman. 2017. I Am Woman, Hear Me Tweet! Gender Differences in Twitter Use among Congressional Candidates. Journal of Women, Politics & Policy 38, 4 (2017), 430–455. https://doi.org/10.1080/1554477X.2016.1268871 https://doi.org/10.1080/1554477X.2016.1268871

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: References

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে জেন্ডার বায়াস উন্মোচন: রেফারেন্স

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Referenzen

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: referencias

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : références

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: संदर्भ

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：参考文献

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 참고자료

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: referências

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: ссылки

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Referanslar

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tài liệu tham khảo

揭示印度推特上记者与政客互动中的性别偏见：参考文献

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 7. CONCLUDING DISCUSSION Misogyny remains an intractable social problem in India and the subcontinent. Subconscious gender bias manifests itself in many spheres of life and in several professions. This study seeks to uncover gender bias against female politicians on Twitter in India using NLP-based emotion analysis and LDA topic modeling- specifically in the interactions between journalists, who are the main drivers of political discourse on Twitter, and politicians. Our findings reveal a disconcerting trend: while popular journalists themselves do not seem to suffer from gender bias, on average, Twitter users display bias against female politicians. This bias is a reflection of the larger societal issue where male politicians traditionally occupy a higher pedestal in politics. This systematic bias is translating onto Twitter, and merely using balanced language is not enough to rectify the situation. To counteract this bias, we suggest utilizing recommender systems to amplify the voices of female politicians, as balanced language alone isn’t sufficient. Additionally, our analysis reveals that users tend to give more weight to the views of male journalists when evaluating female politicians, highlighting the need for diverse voices in political discourse. In summary, this study emphasizes the need to address gender bias on Twitter through not only language but also platform design and algorithms. Creating a more inclusive digital space is crucial to ensuring female public figures can effectively disseminate their views despite societal biases.

Uncovering Gender Bias within Journalist-Politician Interaction: Concluding Discussion

সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: আলোচনা সমাপ্তি

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern: Abschließende Diskussion

Descubriendo el sesgo de género en la interacción periodista-político: discusión final

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques : discussion finale

पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: समापन चर्चा

ジャーナリストと政治家の交流におけるジェンダーバイアスの解明：最終議論

언론인-정치인 상호작용에서 성 편견 발견: 토론 마무리

Descobrindo o preconceito de gênero na interação jornalista-político: discussão final

Выявление гендерных предубеждений во взаимодействии журналистов и политиков: заключительная дискуссия

Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Sonuç Tartışması

Khám phá sự thiên vị về giới trong tương tác giữa nhà báo và chính trị gia: Thảo luận kết luận

揭示记者与政客互动中的性别偏见：总结性讨论

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 6. LIMITATIONS This study focuses only on the top female/male journalists (based on their follower count) on Twitter. These journalists work for reputable media outlets which likely have strong guidelines and policies on gender. Therefore, our results underestimate the gender bias against politicians on Twitter. A more granular analysis of the tweets of lesser known journalists and their followers may reveal even higher levels of bias and misogyny. A similar assessment can be made on our choice of politician. We focus on the top 50 politicians of each gender in our analysis. These politicians are of considerable standing and thus less likely to face bias in their interactions with journalists. This may not be true in situations where journalists interact with female politicians of lower standing and political clout. Thus by focusing on the top fifty female politicians, we underestimate the bias that average female politicians may be subjected to on Twitter. Lastly, our analysis also reveals that there might be bias against female journalists on Twitter. This study does not comprehensively address this, especially in the context of their interactions with male and female politicians. We expect to address this in future work. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 6. LIMITATIONS This study focuses only on the top female/male journalists (based on their follower count) on Twitter. These journalists work for reputable media outlets which likely have strong guidelines and policies on gender. Therefore, our results underestimate the gender bias against politicians on Twitter. A more granular analysis of the tweets of lesser known journalists and their followers may reveal even higher levels of bias and misogyny. A similar assessment can be made on our choice of politician. We focus on the top 50 politicians of each gender in our analysis. These politicians are of considerable standing and thus less likely to face bias in their interactions with journalists. This may not be true in situations where journalists interact with female politicians of lower standing and political clout. Thus by focusing on the top fifty female politicians, we underestimate the bias that average female politicians may be subjected to on Twitter. Lastly, our analysis also reveals that there might be bias against female journalists on Twitter. This study does not comprehensively address this, especially in the context of their interactions with male and female politicians. We expect to address this in future work.

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Limitations

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে জেন্ডার বায়াস উন্মোচন: সীমাবদ্ধতা

Aufdeckung geschlechtsspezifischer Voreingenommenheit bei der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Einschränkungen

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: limitaciones

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : limites

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: सीमाएँ

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：限界

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 한계

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: limitações

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: ограничения

Hint Twitter'ında Gazeteci-Siyasetçi Etkileşiminde Cinsiyet Önyargısını Ortaya Çıkarmak: Sınırlamalar

Khám phá sự thiên vị về giới trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Những hạn chế

揭示印度推特上记者与政客互动中的性别偏见：局限性

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories (p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p-value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter: We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories ( p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. Male Politicians are more frequently mentioned by journalists: p p vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p -value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.1. Emotion analysis. p 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. 5.2.2. Topic analysis. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter : We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. 5.3.1. Inherent Gender Bias in Indian Twitter In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society.

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Results

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: ফলাফল

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern auf Twitter in Indien: Ergebnisse

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resultados

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résultats

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह का खुलासा: परिणाम

インドのツイッターにおけるジャーナリストと政治家のやり取りにおけるジェンダーバイアスの解明：結果

인도 트위터의 언론인-정치인 상호 작용에서 성 편견 발견: 결과

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resultados

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: результаты

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Sonuçlar

Khám phá thành kiến giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Kết quả

揭示印度推特上记者与政客互动中的性别偏见：结果

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Abstract & Intro

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ মিথস্ক্রিয়া মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: বিমূর্ত এবং ভূমিকা

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern im indischen Twitter: Zusammenfassung und Einführung

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resumen e introducción

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résumé et introduction

भारतीय ट्विटर पर पत्रकार-राजनेता के बीच बातचीत में लैंगिक पूर्वाग्रह को उजागर करना: सार और परिचय

インドのツイッターにおけるジャーナリストと政治家の交流におけるジェンダーバイアスの解明：概要と序文

인도 트위터의 언론인-정치인 상호작용에서 성 편견 발견: 요약 및 소개

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resumo e introdução

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: Аннотация и введение

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Özet ve Giriş

Khám phá sự thiên vị giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Tóm tắt & Giới thiệu

揭示印度推特上记者与政客互动中的性别偏见：摘要与简介

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