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.

社交媒体及其新闻推送算法对于提升构建性对话具有挑战性，本研究使用大型语言模型和基于代理模型的仿真来研究不同的新闻推送算法如何影响在线对话质量，发现新推送算法能够促进跨政治观点的建设性、非有害对话。