We are currently living in the age of information and data. Whether it be through news outlets or social media, everyday we are bombarded with hundreds of images and headlines that come in the shape of articles, personal stories, memes or videos. In this busy landscape, “fake news” and misinformation have become an increasing tendency, almost the “new normal”, with far-reaching consequences including impacting the results of the 2016 United States election and, more recently, the spreading of false information around the COVID-19 pandemic. 

To counter these issues, many fact-checking initiatives (e.g. Fact Check, Politifact, The Trust Project and Snopes) have emerged to help identify when a post, article or video contains dubious or fake content. However, this task – which usually requires careful human inspection -is a slow and inefficient process, especially in terms of preventing false information from going viral. In that regard, Facebook’s strategy is interesting, if lacking. The company recently added a tool that allows users to flag news-feed posts as “fake news”. When a large number of people do this, the post appears on other users’ feed with less frequency and carries a warning. Unfortunately, user-generated reporting can backfire and lead to censorship, as experts believe has been the case for the Syrian government opposition. In this context of socio-economic polarization, lack of regulation and low will from the private sector, “fake news” (which are also easy to create) continue to proliferate. Moreover, since this kind of content is not only produced by humans, but also by bots and Artificial Intelligence, identifying, controlling and tracking misinformation has become a complex and surmounting endeavour.

Big Data and Artificial Intelligence approaches have been leveraged to tackle this phenomenon by creating models to automatically decipher false information and prevent it from spreading. For example, professor Maite Taboada from Simon Fraser University has focused her research in making social media and online-platform discussions a more reliable and safe space for communication by working at the intersection of linguistics, computational linguistics and data science fields. However, Machine Learning projects have not all been used to fight misinformation, but rather in some cases – as what happened with the telecom provider Vittel–, to facilitate the propagation of  deep-fake videos or the use of OpenAI GPT-2 to automatize “fake news”.

To explore the danger of misinformation and the tools and strategies being used to stop them from propagating, this edition of Links We Like addresses the topic by exploring some of the key challenges, limitations and opportunities brought forward by this developing field of work.