  Polarization, declining trust, and wavering support for democratic norms are
pressing threats to U.S. democracy. Exposure to verified and quality news may
lower individual susceptibility to these threats and make citizens more
resilient to misinformation, populism, and hyperpartisan rhetoric. This project
examines how to enhance users' exposure to and engagement with verified and
ideologically balanced news in an ecologically valid setting. We rely on a
large-scale two-week long field experiment (from 1/19/2023 to 2/3/2023) on
28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users
tweeting about sports, entertainment, or lifestyle with a contextual reply
containing two hardcoded elements: a URL to the topic-relevant section of
quality news organization and an encouragement to follow its Twitter account.
To further test differential effects by gender of the bots, treated users were
randomly assigned to receive responses by bots presented as female or male. We
examine whether our over-time intervention enhances the following of news media
organization, the sharing and the liking of news content and the tweeting about
politics and the liking of political content. We find that the treated users
followed more news accounts and the users in the female bot treatment were more
likely to like news content than the control. Most of these results, however,
were small in magnitude and confined to the already politically interested
Twitter users, as indicated by their pre-treatment tweeting about politics.
These findings have implications for social media and news organizations, and
also offer direction for future work on how Large Language Models and other
computational interventions can effectively enhance individual on-platform
engagement with quality news and public affairs.
