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

This study explores the polarization of news content shared on Facebook compared to email, using data from the New York Times’ Most Emailed and Most Shared lists over 2.5 years. Employing Latent Dirichlet Allocation (LDA) and Large Language Models (LLMs), we find that highly polarized articles are more likely to be shared on Facebook (vs. 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 AI models like ChatGPT, which is both scalable and cost-effective. This research contributes to the evolving intersection of Large Language Models (LLMs), social media, and polarization studies, shedding light on descriptive patterns of content dissemination across different digital channels.