Understanding Differences in News Article Interaction Patterns on Facebook: Public vs. Private Sharing with Varying Bias and Reliability

• URL: http://arxiv.org/abs/2305.11943v2
• Date: Sat, 30 Dec 2023 16:02:43 GMT
• Title: Understanding Differences in News Article Interaction Patterns on Facebook: Public vs. Private Sharing with Varying Bias and Reliability
• Authors: Alireza Mohammadinodooshan, Niklas Carlsson
• Abstract summary: We present the first comprehensive comparison of the interaction patterns and depth of engagement between public and private posts on Facebook. Our findings highlight significant disparities in interaction patterns across various news classes and spheres. The findings of this study can benefit Facebook content moderators, regulators, and policymakers, contributing to a healthier online discourse.
• Score: 2.4294291235324863
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of news dissemination and user engagement on social media has raised concerns about the influence and societal impact of biased and unreliable information. As a response to these concerns, a substantial body of research has been dedicated to understanding how users interact with different news. However, this research has primarily analyzed publicly shared posts. With a significant portion of engagement taking place within Facebook's private sphere, it is therefore important to also consider the private posts. In this paper, we present the first comprehensive comparison of the interaction patterns and depth of engagement between public and private posts of different types of news content shared on Facebook. To compare these patterns, we gathered and analyzed two complementary datasets: the first includes interaction data for all Facebook posts (private + public) referencing a manually labeled collection of over 19K news articles, while the second contains only interaction data for public posts tracked by CrowdTangle. As part of our methodology, we introduce several carefully designed data processing steps that address some critical aspects missed by prior works but that (through our iterative discussions and feedback with the CrowdTangle team) emerged as important to ensure fairness for this type of study. Our findings highlight significant disparities in interaction patterns across various news classes and spheres. For example, our statistical analysis demonstrates that users engage significantly more deeply with news in the private sphere compared to the public one, underscoring the pivotal role of considering both the public and private spheres of Facebook in future research. Beyond its scholarly impact, the findings of this study can benefit Facebook content moderators, regulators, and policymakers, contributing to a healthier online discourse.

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