A ranked solution for social media fact checking using epidemic spread modeling
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.
John H. Smith, Nathaniel D. Bastian, A ranked solution for social media fact checking using epidemic spread modeling, Information Sciences, Volume 589, 2022, Pages 550-563, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2022.01.002.