Identifying Twitter users who repost unreliable news sources with linguistic information.

ABSTRACT: Social media has become a popular source for online news consumption with millions of users worldwide. However, it has become a primary platform for spreading disinformation with severe societal implications. Automatically identifying social media users that are likely to propagate posts from handles of unreliable news sources sometime in the future is of utmost importance for early detection and prevention of disinformation diffusion in a network, and has yet to be explored. To that end, we present a novel task for predicting whether a user will repost content from Twitter handles of unreliable news sources by leveraging linguistic information from the user's own posts. We develop a new dataset of approximately 6.2K Twitter users mapped into two categories: (1) those that have reposted co

SUBMITTER: Mu Y 

PROVIDER: S-EPMC7924477 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature