The Manufacture of Political Echo Chambers by Follow Train Abuse on Twitter

Torres-Lugo, Christopher; Yang, Kai-Cheng; Menczer, Filippo

A growing body of evidence points to critical vulnerabilities of social
media, such as the emergence of partisan echo chambers and the viral spread of
misinformation. We show that these vulnerabilities are amplified by abusive
behaviors associated with so-called ''follow trains'' on Twitter, in which long
lists of like-minded accounts are mentioned for others to follow. This leads to
the formation of highly dense and hierarchical echo chambers. We present the
first systematic analysis of U.S. political train networks, which involve many
thousands of hyper-partisan accounts. These accounts engage in various
suspicious behaviors, including some that violate platform policies: we find
evidence of inauthentic automated accounts, artificial inflation of friends and
followers, and abnormal content deletion. The networks are also responsible for
amplifying toxic content from low-credibility and conspiratorial sources.
Platforms may be reluctant to curb this kind of abuse for fear of being accused
of political bias. As a result, the political echo chambers manufactured by
follow trains grow denser and train accounts accumulate influence; even
political leaders occasionally engage with them.