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.

翻译：极化、信任度下降以及对民主规范支持的不稳定是对美国民主的紧迫威胁。接触经过验证且高质量的新闻可能降低个体对这些威胁的易感性，并使公民对虚假信息、民粹主义和极端党派言论更具韧性。本研究探讨如何在生态有效的环境中提升用户接触和参与经过验证且意识形态平衡的新闻。我们依托一项为期两周的大规模现场实验（2023年1月19日至2023年2月3日），涉及28,457名Twitter用户。我们利用GPT-2创建了28个机器人，这些机器人回复发布有关体育、娱乐或生活方式话题的用户，每条回复包含两个硬编码元素：指向优质新闻机构相关主题版块的URL，以及鼓励用户关注其Twitter账号。为进一步测试机器人性别差异的影响，被暴露用户被随机分配接收由女性或男性身份呈现的机器人回复。我们检验了随时间推进的干预措施是否能提升用户对新闻媒体机构的关注、新闻内容的分享与点赞、以及政治话题的发布与点赞。结果显示，被暴露用户关注了更多新闻账号，且女性机器人组用户比对照组更可能点赞新闻内容。然而，这些结果多数规模较小，且仅限于实验前已发布政治内容的、具有政治兴趣的Twitter用户。这些发现对社交媒体和新闻机构具有启示意义，并为未来研究如何利用大型语言模型及其他计算干预措施有效提升个体在平台上参与高质量新闻和公共事务提供了方向。