Since the 2016 US presidential election, the term “fake news” has become part of our daily vernacular. Fake news has become so prevalent in our society that even the most sophisticated of us fall for it sometimes. It has reached such epidemic proportions that French President Macron recently commented, “we must fight against the virus of fake news.”
In fact, the World Economic Forum ranks this massive digital spread of misinformation among the top future global risks, along with failure to adapt to climate change, organized crime, and the food shortage crisis.
We’re living in an information “arms race” where media and technology companies rush to develop new defenses as fake news techniques proliferate.
But who should fix these problems? The first thing to establish is who’s responsible for fake news. Some people like to blame social media and their lack of response to this new epidemic. Others like to add that it is in the nature of fake news to be extremely shareable — exactly like the legitimate content the news media has created its new distribution models around.
In my opinion, improving the ability of platforms and media to address the fake news phenomenon requires taking a more holistic approach, identifying areas in every part of the value chain — from creation all the way to circulation and discovery — that need to change.
As the problem has become more widely acknowledged, fighting fake news has proven to be fertile ground for startups. There are lots of different startups fighting fake news, and they’re going about it in many different ways. For instance, some aim to tackle the huge challenge of fighting falsehoods on social media platforms, while others aim to protect brands and their reputation from misinformation. As a result, there are a number of different markets in which startups have the opportunity to succeed.
In order to pinpoint where these opportunities and challenges lie, I’ve broken down fake news into four distinct stages: 1) creation, 2) publication, 3) circulation, and 4) discovery. I’ll discuss those stages below and highlight startups working to address various problems within each stage.
An attacker looking to spread misinformation will need to create content that they can then distribute widely. To do this, they can employ two broad methods: 1) create fake content and/or 2) create fake accounts with which to spread fake content. An example of a company attacking the fake content issue is Knowhere, an AI powered news site, which generates unbiased news stories. Looking at all sites (left-leaning, right-leaning, etc.), Knowhere writes its own “impartial” version of a story based on what it finds. Tackling fake accounts, another company, PerimeterX, tracks the behavior of website visitors and uses artificial intelligence to determine which are bots and which ones should be blocked. Operating under the thesis that fake news has an army of distribution bots at its core, PerimeterX aims to stop malicious bots in their tracks before they attack.
Once content has been created, an attacker will need to publish their misinformation online. Most fake-news-fighting companies operate at this stage, looking to tackle the challenge of deciphering between fact or fiction. Although this is technologically a hard problem to solve given the nuances of human understanding, I view the startups operating solely in this stage as projects rather than ventures, as there is no clear business model and it’s very much a game of whack-a-mole that requires automation at scale. As a result, you see grassroots efforts like the Fake News Challenge, a competition run by volunteers in the AI community, fostering the development of tools that help fact-checkers do their jobs more efficiently.
Like any good news story, fake content will be shared, liked, reposted, and distributed across many different platforms and channels. And while the impact on individual consumers might not be great, advertisers can lose business by hosting bad content and brands can be damaged by being displayed next to bad content. In fact, a June 2017 study from Yahoo’s Brightroll found that 96% of programmatic decision-makers are concerned about fake news as it relates to their business. Of those surveyed, 31% said they will respond to the issue by reducing spend with programmatic partners whose inventory associates brands with fake news. As such, startups like Israeli-based AdVerif.ai are looking to help ad agencies not only identify fake stories to ensure their brand doesn’t appear alongside them, but also defund the bad actors who are producing low-quality content.
The more widely a piece of misinformation can be spread, the better the chances that it will capture the public imagination and achieve its objective. In order to prevent the discovery of such content, some startups are taking a decentralized approach whereby an editorial board no longer determines what’s worth reading: instead, users, developers, and publishers run custom rankings to produce search results. One such company is Userfeeds, which is using Blockchain technology to create transparent and publicly auditable content networks and ranking algorithms. A decentralized platform gives publishers and application developers new business models, outside of advertising and subscriptions, and potentially improves “organic” content discovery for audiences that doesn’t rely on easily abused social media signals such as links, likes, and votes.
The companies I highlighted above were startups operating in singular stages, but if you take a look at the market map, you’ll notice three companies operating more as a full-stack solution, whereby the output of one stage is fed as inputs into another to create a data feedback loop, a competitive moat.
It’s clear that, as attackers continue to evolve their techniques and innovate, so too will defenders, whether it’s:
And as I think through the opportunities for why these companies are the most likely to succeed, I boil it down to three core themes:
Similar to the way a computer virus spreads from host to host, misinformation spreads like wildfire from person to person. For example, a recent MIT study showed that fake news spreads six times faster than the truth on Twitter. Continuing this virus analogy, I see companies tackling fake news in the same way they responded to anti-virus software back in the 1990s and 2000s; Symantec is an example of an appropriate consumer-plus enterprise business model worth replicating today.
By providing a free consumer service, a company that roots out and identifies fake news can gather data at little to no cost and build up a base of knowledge about where all harmful content resides. This data can then be sold to enterprises, providing a layer of analytics and insights. The consumer product is thus a means to the end of selling to businesses.
Once the service compiles this data, one potential commercialization can be around brand security. The social media management / analytics market, of which reputation management is a sub-segment, is estimated to grow to $9.54B by 2022 and is driven largely by the increased access to information. Brands today are unaware of where they are going to appear and know that their reputation can be damaged in a very short time. Their brand may appear next to undesirable or inappropriate content, or other content may spread unfair, deceptive, or non-credible information about them. Companies are increasingly looking for solutions that not only protect their brands but give them insights into how their brands are being portrayed.
At this point, I’ve seen too many startups building businesses around a fact-checking service that operates as some extension of Google Chrome. The problem with whitelisting news sources is that you’re handing the advantage to incumbent news sources — which means there can be very little innovation in this space.
What I’m much more interested in seeing are startups moving up the stack toward the source of the content and trying to identify the coordination of activities rather than the veracity of the data, which is a much smaller market.
If you have a network of things being shared and a network of people sharing them, there are orders of magnitude more people sharing “interesting” things than things being shared. The content itself doesn’t change nearly as fast as users do. Moreover, anybody identifying content runs into a slippery problem: despite its prevalence, identifying fake news is highly subjective. The term “fake news” has so many definitions and requires such nuances of human understanding that competent fact checking becomes a sort of AI-complete problem.
As such, the biggest room for improvement is in detecting different types of coordination, the signature of behavior. Due to the sheer scale of information online, people have yet to try all the possible ways to cluster accounts in a network. Imagine identifying clusters not only by the content but also by comparing stylometry, topicality, timing, and other characteristics. By analyzing the network propagation of stories and trying to use that heuristic to classify things as fake, companies can avoid having to determine what is ground truth.
In my view, companies that aim to create a more holistic, trustworthy news platform have a higher chance of succeeding than those looking to build the next crowdsourced fact-checking service or bias-detection algorithm.
Pushing values of truth can take many forms. One example might be building a reputation score that indicates how much the reader can trust a piece of content.
Having a reputation system that identifies and differentiates trusted sources of information from bots and from misinformation could be an invaluable tool for consumers trying to make informed decisions.
I liken this to New York City’s requirement that restaurants post calorie counts on their menus. This not only encouraged New Yorkers to eat more healthfully, but — an unintended consequence — also forced restaurants to introduce lower-calorie items. Similarly, by having a reputation score on each piece of content, users would be able to see which sources and content they could believe, implicitly putting pressure on the content producers who care about their reputation.
Just as the availability of food metrics like calories and nutrients helps drive better decision-making and changes public health at a meta level, metrics that scale up to increase the authority of accurate news will drive more accurate thinking about a multiplicity of information sources.
Reputation leads to credibility, which leads to trust.
The internet has democratized publishing, but it has not made editing for the common good — that is, optimal consumer education and media literacy — a value or priority. Whether it’s coming up with new models of distribution or building out security products over time, I too resonate with French President Emmanuel Macron when he says, “We must fight against the virus of fake news.”
Ultimately, I believe this space will be a blend of media and cybersecurity, in which companies will have rethought the distribution of true news while innovating technologically to stop the spread of fake news. If you think about it, cybersecurity attacks such as viruses and DDoS operate the same way, but instead now leverage the social media networks that have come to fruition over the past eight years. And startups that end up taking a multidisciplinary approach to prevention and look to tackle one or more of the three opportunities discussed above will triumph in the fake news arms race.
If you’re building a company that is looking to join the battle or have a startup to recommend in any of the above categories, drop me an email at firstname.lastname@example.org.
I’d love to chat.
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