Title: The spread of low-credibility content by social bots,
Abstract: The massive spread of digital misinformation has been identified as a major
global risk and has been alleged to influence elections and threaten
democracies. Communication, cognitive, social, and computer scientists are
engaged in efforts to study the complex causes for the viral diffusion of
misinformation online and to develop solutions, while search and social media
platforms are beginning to deploy countermeasures. With few exceptions, these
efforts have been mainly informed by anecdotal evidence rather than systematic
data. Here we analyze 14 million messages spreading 400 thousand articles on
Twitter during and following the 2016 U.S. presidential campaign and election.
We find evidence that social bots played a disproportionate role in amplifying
low-credibility content. Accounts that actively spread articles from
low-credibility sources are significantly more likely to be bots. Automated
accounts are particularly active in amplifying content in the very early
spreading moments, before an article goes viral. Bots also target users with
many followers through replies and mentions. Humans are vulnerable to this
manipulation, retweeting bots who post links to low-credibility content.
Successful low-credibility sources are heavily supported by social bots. These
results suggest that curbing social bots may be an effective strategy for
mitigating the spread of online misinformation.