Current events have brought the spread of false information via social media to the forefront of national consciousness. With Mark Zuckerberg’s ongoing testimony on Capitol Hill regarding Facebook’s role in the proliferation of fake news giving us insight into what happened, it is important to identify the how, i.e., the mechanism by which misinformation goes viral. Using machine learning, complex network theory, and methods such as Granger causality on a massive Twitter dataset numbering 171 million relevant Tweets, members of our lab uncovered patterns in how various sources of fake news influenced– and were influenced by– their followers.
We find, for example, that unlike the influential spreaders of traditional news, which are largely the Twitter accounts of verified sources such as the New York Times or CNN, the influencers in the fake news dataset are largely users whose accounts were not verified or have since been deleted. Moreover, while the opinions of users identified as being more left-leaning seemed to be influenced by traditional centrist or left-leaning media sources’ coverage of the Democratic candidate Hillary Rodham Clinton, the opinions of users identified to be more right-leaning were found to actually have an influence on the fake news influencers.
CCNY physicist tracks influence of fake news on Presidential election CCNY News (2018), Rebecca Rivera
Influence of fake news in Twitter during the 2016 US Presidential election arXiv (2018), Alexandre Bovet and Hernán A. Makse