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

2024-09-20

AI Summary

This study investigates how Facebook’s U.S. users’ news engagement behavior from 2017–2020 contributed to escalating ideological segregation and declining news quality. Method: Leveraging privacy-preserving URL interaction data and integrating multi-source annotations (e.g., Media Bias/Fact Check), we construct a dynamic, user-level news consumption quality assessment framework weighted by inferred political preferences—quantifying ideological drift and quality trajectories across liberal, conservative, and centrist cohorts. Contribution/Results: We provide the first systematic evidence that two major Facebook News Feed algorithm updates triggered non-monotonic effects: an initial phase featuring widening ideological gaps, deteriorating news quality, yet increased engagement; followed by a subsequent phase with further polarization, persistent quality decline, and reduced engagement. These findings empirically demonstrate that algorithmic interventions significantly exacerbate both ideological polarization and informational degradation in online news ecosystems, offering robust evidence to inform platform governance and algorithmic accountability.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Machine Learning: Learning Preferences or Rankings

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Analyzes ideological segregation in news consumption
• Tracks news quality changes on Facebook
• Links engagement trends to News Feed updates

Innovation

• Facebook URL Dataset analysis
• Ideological segregation tracking
• News quality evaluation metrics