Abstract
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.
Abstract (translated)
极化、信任下降以及支持民主规范的不稳定支持，对美国民主构成了严重的威胁。接触到的经过验证和质量的新闻可能会降低个人对这些威胁的易感性，使公民对虚假信息、极化和极端派系言论更具韧性。本项目旨在探讨如何在生态可行的环境中提高用户接触和参与经过验证和思想平衡的新闻。我们依靠一个针对28,457名Twitter用户的2周大规模的现场实验（从2023年1月19日至2月3日）。我们创建了28个使用GPT-2的机器人，它们在用户推特关于体育、娱乐或生活方式时回复，内容包含两个硬编码元素：主题相关高质量新闻组织的URL和鼓励关注其Twitter账号的提示。为了进一步测试机器人的不同性别效果，我们将被随机分配接受机器人回复的用户。我们研究了我们的时间干预是否增加了新闻媒体组织的关注、新闻内容的分享和喜欢，以及关于政治的推特和喜欢政治内容的推特。我们发现，经过治疗的用户关注了更多的新闻账户，女性机器人治疗的用户比控制用户更可能喜欢新闻内容。然而，绝大多数结果的规模较小，仅限于已经具有政治兴趣的Twitter用户，这表明他们在推特上关于政治的推特。这些发现对社交媒体和新闻机构具有影响，并为未来在大型语言模型和其他计算干预上如何有效增强用户在高质量新闻和公共事务上的在线参与提供了方向。
URL
https://arxiv.org/abs/2403.13362
