Analyzing News Engagement on Facebook: Tracking Ideological Segregation and News Quality in the Facebook URL Dataset

• URL: http://arxiv.org/abs/2409.13461v2
• Date: Tue, 18 Feb 2025 16:21:50 GMT
• Title: Analyzing News Engagement on Facebook: Tracking Ideological Segregation and News Quality in the Facebook URL Dataset
• Authors: Emma Fraxanet, Andreas Kaltenbrunner, Fabrizio Germano, Vicenç Gómez,
• Abstract summary: The dataset has been used in several studies to analyze the relationship between social media engagement and societal issues such as misinformation, polarization, and the quality of consumed news. We conduct a comprehensive analysis of the engagement with popular news domains, covering four years from January 2017 to December 2020, with a focus on user engagement metrics related to news URLs in the U.S. Our findings provide empirical evidence to better understand user behavior, polarization, and misinformation during the period covered by the dataset.
• Score: 3.443622476405787
• License:
• Abstract: The Facebook Privacy-Protected Full URLs Dataset was released to enable independent, academic research on the impact of Facebook's platform on society while ensuring user privacy. The dataset has been used in several studies to analyze the relationship between social media engagement and societal issues such as misinformation, polarization, and the quality of consumed news. In this paper, we conduct a comprehensive analysis of the engagement with popular news domains, covering four years from January 2017 to December 2020, with a focus on user engagement metrics related to news URLs in the U.S. By incorporating the ideological alignment and quality of news sources, along with users' political preferences, we construct weighted averages of ideology and quality of news consumption for liberal, conservative, and moderate audiences. This allows us to track the evolution of (i) the ideological gap in news consumption between liberals and conservatives and (ii) the average quality of each group's news consumption. We identify two major shifts in trends, each tied to engagement changes. In both, the ideological gap widens and news quality declines. However, engagement rises in the first shift but falls in the second. Finally, we contextualize these trends by linking them to two major Facebook News Feed updates. Our findings provide empirical evidence to better understand user behavior, polarization, and misinformation during the period covered by the dataset.

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