PhD Thesis Defense: Jeremy Thompson

Optional ZOOM LINK

"Combating Fake News: A gravity well simulation to model echo chamber formation in social media"

Abstract

Fake news has become a serious concern as distributing misinformation has become easier and more impactful. A solution is critically required, but choosing the right solution is perhaps just as critical. One solution would be to ban fake news, but that approach could create more problems than it solves, and would also be problematic from the beginning, as it must first be identified to be banned. We propose a method to automatically recognize suspected fake news, and to provide news consumers, as well as researchers, historians, and journalists, with more information as to its veracity. It is suggested that fake news is comprised of two primary components: premises and misleading content. A fake news piece can be condensed down to a collection of premises, which may or may not be true, and to various forms of misleading material, including biased arguments and language, misdirection, and manipulation. Misleading content can be exposed for whatever biases it contains, regardless of the intent of the author. While this framework can be valuable, its utility may be limited by the rapid improvement in artificial intelligence, which can be used to alter fake news strategies at a rate that could exceed the ability to update the framework.

Therefore, more immediately, we propose a model for identifying echo chambers, which are widely reported to be havens for fake news producers and consumers. We simulate a social media interest group as a gravity well, through which we can identify the online groups most postured to become echo chambers, and thus a source for fake news consumption and replication. This echo chamber model is supported by three pillars related to the social media group: technology employed, topic explored, and confirmation bias of group members. The model is validated by modeling and analyzing 19 subreddits on the Reddit social media platform. Contributions include a working definition for fake news, a framework for recognizing fake news, a generic model for social media echo chambers including three pillars central to echo chamber formation, and a gravity well simulation for social media groups, implemented for 19 subreddits.

Thesis Committee

• Eugene Santos Jr. (Chair)
• George Cybenko
• Vikrant Vaze
• John Korah (California State Polytechnic University)

Contact

For more information, contact Julia Abraham at julia.s.abraham@dartmouth.edu.