Effects of Political Bias and Reliability on Temporal User Engagement with News Articles Shared on Facebook

Abstract

The proliferation of news on social media platforms has led to concerns about the impact of biased and unreliable information on public discourse. This study examines differences in interaction patterns between public and private sharing of news articles on Facebook, focusing on articles with varying bias and reliability, as well as the depth of interactions. To analyze these patterns, we employed two complementary data collection methods using the CrowdTangle browser extension. We collected interaction data across all Facebook posts (private + public) referencing a manually labeled collection of over 30K news articles, as well as interaction data on public posts posted in the forums tracked by CrowdTangle. Our empirical findings, backed by rigorous statistical analysis, reveal significant differences in interaction patterns between public and private sharing across different classes of news in terms of bias and reliability, highlighting the role of user preferences and privacy settings in shaping the spread of news articles. Notably, we find that irrespective of news class, users tend to engage more deeply in private discussions compared to public ones. Additionally, Facebook users engage more deeply with content from the Right-biased class, and exhibit higher deep interaction ratio levels with content from the Most-unreliable class. This study is the first to directly compare the dynamics of public and private sharing of news articles on Facebook, specifically examining the interactions and depth of engagement with articles of varying bias and reliability. By providing new insights and shedding light on these aspects, our findings have significant implications for understanding the influence of social media on shaping public discourse.