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Political campaign scholars have been concerned about the nature and effects of negative messages. Although much research has examined attack advertisements in broadcast media, more research is needed to understand attack messaging on social media. We conducted a content analysis of campaign tweets generated by candidates who ran for governor in 2014 in the United States. We examine the variables that relate to campaign attack message posting and predict its spread. Analysis suggests challengers are more likely to attack, but incumbent-generated messages are more likely to spread. We also found that attack messages are more likely than advocacy messages to be retweeted.

Zooming in on automated and semiautomated social actors created to influence public opinion on social media, we employ a novel analytic approach to identify patterns of inauthentic behavior across election campaigns on Twitter. Comparing two recent national election campaigns, the 2016 U.S. presidential election and the 2017 German federal election, we analyze patterns and effects of orchestrated intervention in political discourse on Twitter. Focusing on two main aspects of information flows—scale and range—we find that orchestrated interventions help amplify, but not diffuse, the candidates' messages, mostly failing to reach new audiences in the process. This study adds an information diffusion perspective to a growing body of literature on computational propaganda, showing that although false amplification is quite effective in increasing the scale of information events, in most cases the information fails to reach new depths.