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。

使用大型语言模型模拟社交媒体，评估替代新闻订阅算法

Simulating Social Media Using Large Language Models to Evaluate  Alternative News Feed Algorithms

The Web has become the main source for news acquisition. At the same time,
news discussion has become more social: users can post comments on news
articles or discuss news articles on other platforms like Reddit. These
features empower and enable discussions among the users; however, they also act
as the medium for the dissemination of toxic discourse and hate speech. The
research community lacks a general understanding on what type of content
attracts hateful discourse and the possible effects of social networks on the
commenting activity on news articles. In this work, we perform a large-scale
quantitative analysis of 125M comments posted on 412K news articles over the
course of 19 months. We analyze the content of the collected articles and their
comments using temporal analysis, user-based analysis, and linguistic analysis,
to shed light on what elements attract hateful comments on news articles. We
also investigate commenting activity when an article is posted on either
4chan's Politically Incorrect board (/pol/) or six selected subreddits. We find
statistically significant increases in hateful commenting activity around
real-world divisive events like the "Unite the Right" rally in Charlottesville
and political events like the second and third 2016 US presidential debates.
Also, we find that articles that attract a substantial number of hateful
comments have different linguistic characteristics when compared to articles
that do not attract hateful comments. Furthermore, we observe that the post of
a news articles on either /pol/ or the six subreddits is correlated with an
increase of (hateful) commenting activity on the news articles.

本文大规模分析了 19 个月中发布在 412K 个新闻文章上的 1.25 亿条评论，结论是，发布在社交网络上的新闻文章会吸引更多的有害评论，这些评论的语言特点不同于其他评论，而且与一些政治事件有关。