Political Astroturfing on Twitter: How to Coordinate a Disinformation Campaign. (3rd March 2020)

• Record Type: Journal Article
• Authors: Keller, Franziska B.; Schoch, David; Stier, Sebastian; Yang, JungHwan
• Is Part Of: Political communication. Volume 37:Number 2(2020)
• Page Start: 256
• Page End: 280
• Publication Date: 2020-03-03
• Subjects: disinformation -- astroturfing -- election campaign -- propaganda -- social media; Communication -- Political aspects -- Periodicals; Communication in politics -- Periodicals; 306.205
• DOI: 10.1080/10584609.2019.1661888
• Languages: English
• ISSNs: 1058-4609

Abstract

Abstract : Political astroturfing, a centrally coordinated disinformation campaign in which participants pretend to be ordinary citizens acting independently, has the potential to influence electoral outcomes and other forms of political behavior. Yet, it is hard to evaluate the scope and effectiveness of political astroturfing without "ground truth" information, such as the verified identity of its agents and instigators. In this paper, we study the South Korean National Information Service's (NIS) disinformation campaign during the presidential election in 2012, taking advantage of a list of participating accounts published in court proceedings. Features that best distinguish these accounts from regular users in contemporaneously collected Twitter data are traces left by coordination among astroturfing agents, instead of the individual account characteristics typically used in related approaches such as social bot detection. We develop a methodology that exploits these distinct empirical patterns to identify additional likely astroturfing accounts and validate this detection strategy by analyzing their messages and current account status. However, an analysis relying on Twitter influence metrics shows that the known and suspect NIS accounts only had a limited impact on political social media discussions. By using the principal-agent framework to analyze one of the earliest revealed instances of political astroturfing, we improve on extant methodological approaches to detect disinformation campaigns and ground them more firmly in social science theory.