Reddit 新闻讨论中基于人口统计学而非意识形态隔离的证据
Evidence of Demographic rather than Ideological Segregation in News Discussion on Reddit
AI总结：
本文通过分析 Reddit r/news 社区五年互动数据，发现用户倾向于跨意识形态互动而人口统计学群体内部同质，表明情感极化更多源于社会分裂而非网络回音室。
摘要：
我们在一个典型的意见形成情境——当前新闻的在线讨论中，评估意识形态群体和人口统计学群体之间的同质性与异质性。我们分析了美国访问量最大的网站之一 Reddit 上 r/news 社区中用户跨越五年的互动。随后，我们估计了这些用户的人口统计学属性和意识形态属性。借助与精心构建的网络零模型的比较，我们确定了哪些属性组合会促进互动，哪些会抑制互动。个体更倾向于与意识形态相反的一方互动，这与回音室叙事相矛盾。相反，人口统计学群体表现出同质性，即个体倾向于在自己的群体内互动——即使是在这类属性无法直接观察到的在线环境中也是如此。特别是，我们观察到年龄和收入隔离在多年间持续存在：当用户属于不同群体时，他们倾向于避免互动。在控制了各人口统计学群体对不同新闻主题的兴趣程度之后，这些结果仍然成立。我们的发现与以下理论一致：情感极化——跨越政治界限进行社交的困难——更多地与日益分裂的社会相关，而非社交媒体上的意识形态回音室。我们公开发布了匿名化数据集以及用于复现结果的全部代码：https://github.com/corradomonti/demographic-homophily
英文摘要：
We evaluate homophily and heterophily among ideological and demographic groups in a typical opinion formation context: online discussions of current news. We analyze user interactions across five years in the r/news community on Reddit, one of the most visited websites in the United States. Then, we estimate demographic and ideological attributes of these users. Thanks to a comparison with a carefully-crafted network null model, we establish which pairs of attributes foster interactions and which ones inhibit them. Individuals prefer to engage with the opposite ideological side, which contradicts the echo chamber narrative. Instead, demographic groups are homophilic, as individuals tend to interact within their own group - even in an online setting where such attributes are not directly observable. In particular, we observe age and income segregation consistently across years: users tend to avoid interactions when belonging to different groups. These results persist after controlling for the degree of interest by each demographic group in different news topics. Our findings align with the theory that affective polarization - the difficulty in socializing across political boundaries-is more connected with an increasingly divided society, rather than ideological echo chambers on social media. We publicly release our anonymized data set and all the code to reproduce our results: https://github.com/corradomonti/demographic-homophily
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
