Automated Twitter accounts, called bots, helped spread bogus articles during and after the 2016 U.S. presidential election by making the content appear popular enough that human users would trust it and share it more widely, researchers report online November 20 in Nature Communications. Menczer’s team then used Botometer, a computer program that learned to recognize bots by studying tens of thousands of Twitter accounts, to determine the likelihood that each account in the dataset was a bot. The researchers found that in the first few seconds after a viral story appeared on Twitter, at least half the accounts sharing that article were likely bots; once a story had been around for at least 10 seconds, most accounts spreading it were maintained by real people.