While many claim that social media websites facilitate the spread of fake news, current research overlooks the role that content written by social media users plays in misinforming the public. Facebook posts and tweets can contain false information without being subject to rigorous fact-checking. This means that individuals may be exposed to inaccurate information from their online friends without even knowing that they should question it. In this project, I propose to study the extent to which user-generated content on social media platforms distorts information reported by mainstream news outlets. Using a variety of natural language processing methods, I will examine the text of social media posts made by news outlets, as well as the text of comments on those posts, between 2016 and 2020. In an effort to understand which types of news content are more likely to become distorted in the comments, I will conduct an experiment designed to capture the causal effect of the initial news source and the salience of the topic on information distortion. This project stands to change the way we think about misinformation on social media and point toward the need for a different toolkit for trying to combat the problem.
Assistant Professor, Washington University in St. Louis