From Feeds to Inboxes: A Comparative Study of Polarization in Facebook and Email News Sharing

• 成果类型: Article
• 署名作者: Yoganarasimhan, Hema; Iakovetskaia, Irina
• 署名单位: University of Washington; University of Washington Seattle; Stanford University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.04134
• 发表日期: 2024
• 页码: 6461-6472
• 关键词: polarization; Social media; large language models (LLMs); facebook; news

摘要

This study explores the polarization of news content shared on Facebook compared with email using data from the New York Times' ' Most Emailed and Most Shared lists over 2.5 years. Employing latent Dirichlet allocation and large language models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (versus email), even after accounting for factors like topics, emotion, and article age. Additionally, distinct topic preferences emerge, with social issues dominating Facebook shares and lifestyle topics prevalent in emails. Contrary to expectations, political polarization of articles shared on Facebook did not escalate post-2020 election. We introduce a novel approach to measuring polarization of text content that leverages generative artificial intelligence models, like ChatGPT, and it is both scalable and cost effective. This research contributes to the evolving intersection of LLMs, social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels.