A Ranked Solution for Social Media Fact Checking Using Epidemic Spread Modeling
USMA Research Unit Affiliation
Army Cyber Institute
Within the past decade, social media has become a primary platform for consumption of information and current events. Unlike with traditional news sources, however, social media posts do not have to go through a rigorous validation process prior to publication. The 2019 Mueller Report illustrates how malicious actors have taken advantage of these lax requirements to sway public opinion on topics from the #blacklivesmatter movement to the 2016 U.S. Presidential election. Currently, social media companies rely primarily on communal-policing of misinformation; it is unlikely that this will happen with regularity. To counteract this, other literature on the topic is focused on using deep learning models to separate accurate from misleading content; however, the rapidly evolving nature of misinformation means that they will have to be retrained and redeployed on an iterative and time-consuming basis. This work, therefore, proposes a novel approach to the problem: treating misinformation as a virus. Specifically, we propose a ranking system that third-party fact checkers can utilize to prioritize posts for checking. This algorithm is then tested against multiple data sets with strong positive results, decreasing viral spread in a matter of minutes.
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