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Tackling Fake News: A Battle of Economic Incentives and Disincentives

It is very likely that this is not the first article about fake news that you read. The phenomenon has become viral in the past years, and its consequences, from elections outcomes to fuelling propaganda and hateful speech, have been widely discussed. The concept of fake news – which means any information created to mislead the public into doubting truths and believing falsehoods – became viral during the 2016 US electoral campaign. Shortly before the US elections, a group of Buzzfeed’s media editors identified a stream of made-up online stories about the American candidates and their parties. The bulk of such stream, as the editors discovered, originated in the city of Veles Macedonia, where more than 140 fake US-elections-news sites were created at the time. Most of these websites published pro-Trump content aimed at republican supporters in the US.

What is interesting about such a phenomenon is that creators of the fake news had little interest in the outcome of the elections. Instead, the incentive behind the fake websites was purely economic: by publishing sensationalist and often false content on Facebook, writers realised they generated the most shares, and therefore the highest revenue. The impact of these news was astonishing: fake election news outperformed engagement on Facebook, as compared to most successful election stores from 19 news outlet combined. In fact, it has been reported that some of the individuals publishing these fake stories made substantial sums of money from their efforts – up to tens of thousands of US dollars.

The impact of fake news is outstanding. Research has shown that misinformation is as likely to go viral as reliable information, partly because the structure of social networks as a whole is not effective in filtering reliable from false information. Furthermore, as people pay more attention to a certain trending topic, whether true or false, more information on that topic will be produced. In a way, fabricating information is cheaper and easier than reporting the actual truth, and fabrication can be tailored to any group. For example, during the US election campaign in 2016, conservative sources claimed that the Pope endorsed Trump and liberal sources that he endorsed Clinton. The truth is, he did neither.

Economic Disincentives against Fake News – The Way to Go?

By assuming that the motivation that fuels fake-news editors are mainly economic, it is logical to conclude that economic disincentives could help in reducing the work of these. In Germany, for example, the Netzwerkdurchsetzungsgesetz (NetzDG) law on media content requires platforms such as YouTube, Facebook and Twitter to remove false information or hate speech posts within 24 hours, with fines of up to 50 million euros for non-compliance. Another example can be found on Google: as a result of the 2016 false information stream, Google decided to prohibit fake news websites from using AdSense to make money off targeted advertisements. This has enabled Google to stop hundreds of websites since then, and Facebook is also planning to take similar action.

The problem is that, while these market-based solutions show steps in the right direction, they are not effective on their own for a variety of reasons. First, they fail to capture fake news websites where the incentive is not money, but rather ideology or power. Second, economically disincentivising fake-news websites ran in social media platforms (whether is Facebook, Twitter or any other) may clash with the economic incentives of the online platforms themselves.

An Alternative Economic Approach: Price Mechanisms

Existing methods to control fake news in social media have relied on ex-post-detection, fact-checking and removal of offensive content. These have proven to be ineffective, simply because online platforms structure is not made to filter content. Social media works on the basis that information will keep spreading – its business models often focus on monetising the views rather than the content itself, an approach that inevitably favours content virality over quality. An alternative way to regulate the content published in social media is that these online platforms change their business model and implement a price mechanism. In this approach, social media platforms could require users to pay a small fee each time they publish or share a post. This may sound unachievable, but it is worth to think about. After all, we are already paying the cost of social media by giving up our own personal data and information. We could instead pay directly for the content we are really interested in.

There remain concerns on how effective a model of self-regulation could be in regulating information, and how profitable would it prove for online media companies. But the attractiveness of the idea remains precisely in the concept of self-regulation: by paying for the content published, self-regulation would be delegated to media users. This would allow social media companies to earn the revenue from user posts, as well as to save in content policing and public relations. Furthermore, a price mechanism method would make users think twice before posting (i.e. paying). Ultimately, a pay-per-post model would reduce the amount of content on social media, but it would arguably improve quality and increase user engagement.

Final Thoughts

Ever since the events of the US 2016 elections campaign, countries have sought different ways to counteract fake news. Governments have called on existing laws or created new legislation to sanction fake news. Social media platforms have relied on post-detection and removal of false content. Unfortunately, such measures may be subject to arbitrary interpretation and enforcement, thus, repressing critical independent media and causing too much information to end up being blocked. Economic disincentives and economic self-regulation may prove to be a more effective option without having to restrict freedom of expression and speech. But until now, which method will prove to be most effective in tackling false information remains yet to be seen. Experimentation is highly encouraged and perhaps the rise of Artificial Intelligence and new technologies will help find a definitive solution.


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