在社交媒体平台上使用大型语言模型和现实的机器人账户激励新闻消费
Hadi Askari1, Anshuman Chhabra2, Bernhard Clemm von Hohenberg3
1Department of Computer Science, University of California, Davis, USA.
PNAS nexus
|September 17, 2024
概括
这项研究探讨了使用机器人在Twitter上分享经过验证的新闻. 虽然一些用户关注更多的新闻账户,但干预对参与度的影响很小,特别是在政治内容方面.
科学领域:
- 计算社会科学 计算社会科学
- 数字媒体研究数字媒体研究
- 政治沟通是政治沟通.
背景情况:
- 两极分化和错误信息的增加威胁着美国的民主规范.
- 接触经过验证,平衡的新闻可以增强公民的弹性.
- 了解在线用户与各种新闻来源的互动关系至关重要.
研究的目的:
- 调查增加用户接触和参与验证,意识形态平衡的新闻的方法.
- 在一个大型社交媒体平台上评估机器人驱动干预的有效性.
- 根据机器人的性别呈现来研究潜在的差异效应.
主要方法:
- 这是一项为期两周的实地实验,涉及28,457名推特用户.
- 部署28个GPT-2驱动机器人来回复与经过验证的新闻链接的非政治推文.
- 随机分配用户给作为男性或女性呈现的机器人.
主要成果:
- 接受治疗的用户关注更多的新闻账户;与女性机器人互动的用户喜欢更多的新闻内容.
- 观察到的效应规模很小,主要受益于已经有政治兴趣的用户.
- 没有发现对喜欢或发布政治内容有重大影响.
结论:
- 机器人驱动的干预可以稍微增加新闻关注,但对更广泛的参与影响有限.
- 干预的影响集中在已经对政治感兴趣的用户中.
- 对于在社交媒体平台上的有效的亲社会计算干预措施,需要进一步的研究.
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