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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