Echo Chambers on Social Media: A comparative analysis
Matteo Cinelli, Gianmarco De Francisci Morales, Alessandro Galeazzi,, Walter Quattrociocchi, Michele Starnini

TL;DR
This study introduces a quantitative method to identify echo chambers on social media, analyzing over 1 billion contents across four platforms, revealing platform-specific differences in echo chamber formation and influence of algorithms.
Contribution
It provides an operational definition of echo chambers and a comparative analysis framework applied to multiple social media platforms.
Findings
Facebook and Twitter exhibit clear echo chambers.
Reddit and Gab show less evidence of echo chambers.
News feed algorithms may promote echo chamber formation.
Abstract
Recent studies have shown that online users tend to select information adhering to their system of beliefs, ignore information that does not, and join groups - i.e., echo chambers - around a shared narrative. Although a quantitative methodology for their identification is still missing, the phenomenon of echo chambers is widely debated both at scientific and political level. To shed light on this issue, we introduce an operational definition of echo chambers and perform a massive comparative analysis on more than 1B pieces of contents produced by 1M users on four social media platforms: Facebook, Twitter, Reddit, and Gab. We infer the leaning of users about controversial topics - ranging from vaccines to abortion - and reconstruct their interaction networks by analyzing different features, such as shared links domain, followed pages, follower relationship and commented posts. Our method…
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Taxonomy
TopicsMisinformation and Its Impacts · Social Media and Politics · Opinion Dynamics and Social Influence
