Cross-Platform Coordinated Networks: The Case of the 2021 German Elections

Numerous studies have recently analyzed coordinated communication networks on social media platforms (Thiele et al., 2023). These networks employ coordination among various accounts to influence and manipulate users and platforms (Chan, 2022). This phenomenon is associated with problematic activities with substantial social and political impacts, like disinformation propagation (Giglietto et al., 2020) and state-backed information operations (Keller, 2019; Starbird et al, 2019; Kulichkina et al., 2022). This paper introduces an innovative R package designed for detecting and analyzing coordinated behavior. Unlike existing tools for identifying coordinated behavior (Giglietto et al., 2020; Graham, 2020), we approach the problem of coordination in the digital media ecosystem, which can be better understood by taking a cross-platform approach. This tool is the first R software capable of detecting coordinated networks sharing various types of content across different platforms. The implemented approach also marks an advancement in studying coordinated networks by providing an inclusive perspective on coordinated activities, encompassing both explicit coordination and organic content sharing, potentially influenced by these networks. This method is especially relevant for in-depth analysis of networks engaged in spreading misinformation, fake news, and digital propaganda. The comprehensive architecture of this R tool provides flexibility and a broad scope for analyzing coordinated activities across various digital landscapes which positions it as a distinctive resource for researchers investigating coordinated communication on social media. On the empirical side, the presentation demonstrates the tool's application by examining cross-platform coordinated behavior on Twitter and Facebook during the 2021 German elections. It utilizes a dataset of approximately 400,000 unique URLs shared in over 800,000 posts in the six weeks preceding the elections. The analysis focuses on these networks, the shared content, and the identification of potential information cascades enabled by coordinated networks. Furthermore, the presentation critically assesses the constraints on empirical research in this field due to the data access limitations of social media platforms. References: Chan, J. (2022). Online astroturfing: A problem beyond disinformation. Philosophy & Social Criticism. Graham, T., & QUT Digital Observatory (2020). Coordination Network Toolkit. Giglietto, F., Righetti, N., & Rossi, L. (2020). CooRnet. detect coordinated link sharing behavior on social media. http://coornet.org. Keller, F. B., Schoch, D., Stier, S., & Yang, J. (2020). Political astroturfing on twitter: How to coordinate a disinformation campaign. Political communication, 37(2), 256-280. Kulichkina, A., Righetti, N., & Waldherr, A. (2022). Pro-democracy and Pro-regime Coordination in Russian. Protests: The Role of Social Media. 72nd Annual ICA Conference. Starbird, K., Arif, A., & Wilson, T. (2019). Disinformation as collaborative work: Surfacing the participatory nature of strategic information operations. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1-26. Thiele, D., Milzner, M., Heft, A., Gong, B., & Pfetsch, B. (2023). 50 Shades of Astroturf and How to Choose Them: A Theoretical Model and Review of Coordinated Social Media Manipulation. 72nd Annual ICA Conference.