Social media is often criticized for amplifying toxic discourse and discouraging constructive conversations. But designing social media platforms to promote better conversations is inherently challenging. This paper asks whether simulating social media through a combination of Large Language Models (LLM) and Agent-Based Modeling can help researchers study how different news feed algorithms shape the quality of online conversations. We create realistic personas using data from the American National Election Study to populate simulated social media platforms. Next, we prompt the agents to read and share news articles - and like or comment upon each other's messages - within three platforms that use different news feed algorithms. In the first platform, users see the most liked and commented posts from users whom they follow. In the second, they see posts from all users - even those outside their own network. The third platform employs a novel "bridging" algorithm that highlights posts that are liked by people with opposing political views. We find this bridging algorithm promotes more constructive, non-toxic, conversation across political divides than the other two models. Though further research is needed to evaluate these findings, we argue that LLMs hold considerable potential to improve simulation research on social media and many other complex social settings.

翻译：社交媒体常因放大有害对话并阻碍建设性交流而受到批评。然而，设计能够促进更好对话的社交媒体平台本身极具挑战性。本文探讨是否可以通过结合大型语言模型（LLM）与基于主体的建模来模拟社交媒体，从而帮助研究者研究不同新闻推送算法如何塑造在线对话质量。我们利用美国国家选举研究中的数据创建了逼真的人物角色，用以填充模拟社交媒体平台。随后，我们引导这些智能体在三种采用不同新闻推送算法的平台上阅读并分享新闻文章，同时对他人的消息进行点赞或评论。在第一种平台上，用户仅能看到其关注者中获赞和评论最多的帖子；第二种平台则展示所有用户的帖子——即使对方不在自己的社交网络中；第三种平台采用了一种新颖的“桥梁式”算法，优先呈现那些受到对立政治观点用户共同喜爱的帖子。研究发现，与其他两种模型相比，这种桥梁式算法能促进跨越政治分歧的更具建设性、更低有害性的对话。尽管这些结论仍需进一步研究验证，但我们认为，大型语言模型在提升社交媒体及其他复杂社会情境的模拟研究方面具有巨大潜力。