  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 quantifies the
existence of echo-chambers along two main dimensions: homophily in the
interaction networks and bias in the information diffusion toward likely-minded
peers. We find peculiar differences across social media. Indeed, while Facebook
and Twitter present clear-cut echo chambers in all the observed dataset, Reddit
and Gab do not. Finally, we test the role of the social media platform on news
consumption by comparing Reddit and Facebook. Again, we find support for the
hypothesis that platforms implementing news feed algorithms like Facebook may
elicit the emergence of echo-chambers.
