Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign

“…level have become more influential, political actors such as governments (King, Pan, & Roberts, 2017; Lukito et al., 2018), hyperpartisan media (Allcott & Gentzkow, 2017; Faris et al., 2017), far-right groups (Marwick & Lewis, 2017) or actors with economic motives (Silverman & Alexander, 2016)…”

“…a “social movement grows when people with grievances meet, agree on a common agenda, and organize for political action” (Howard, 2006, p. 86; see also Bennett & Segerberg, 2013), an astroturfing campaign tries to appear like such an organic expression of public opinion, but is actually centrally…”

“, they tweet on the same days – and groups them together (Hartigan, 1972).”

“Tweets posted by known and suspect NIS accounts should reflect these goals and strategies, and therefore differ significantly from those posted by regular users, political users, and opinion leaders (Broniatowski et al., 2018).”

“In order to prevent this type of shirking, the principal may opt for constant and direct supervision by physically locating all agents in the same office – as done by the “Russian troll factories”(3) – or at least hire them to work during office hours instead of paying by post, as the eponymous “50 cent party” alleged…”

“Social media also have the potential to set the news agenda (Chadwick, 2013; Neuman, Guggenheim, Jang, & Bae, 2014) as journalists pay close attention to social media buzz (Lukito et al.”

“To better understand how the patterns of the two types of campaigns should differ, we turn to principal-agent theory (Miller, 2005; Ross, 1973).”

“For the last decade, digital communication technologies have lowered the costs and barriers to connecting with the public, and interest groups have shifted to this online focus (Keller et al. 2020; Stein 2009; Obar et al. 2012; Karpf 2010; Sin and Lupia 2003; Theocharis et al. 2022).”

“These troll-defining characteristics include spam dissemination [12], repetitive or coordinated posting [13], mocking of other users [12], or the expression of aggression [14].”

“11 77 / 1071181320641360 user accounts (Keller et al., 2020; Zannettou et al., 2019), and anomalous activity patterns (Glenski et al., 2019; Wang et al., 2019).”