Incentivizing news consumption on social media platforms using large language models and realistic bot accounts

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 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. Treated users were randomly assigned to
receive responses by bots presented as female or male. We examine whether
our intervention enhances the following of news media organization, the
sharing/liking of news content and the tweeting/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.