Abstract
Increased use of and reliance on social media has led to a responsive rise in the creation of automated accounts on such platforms. Recent approaches to identification of individual automated accounts has relied on machine learning methods utilizing features drawn predominantly from text content and profile metadata. In this work we explore a novel use of graph theoretic measures, specifically common enemy graphs, to identify and characterize groups of accounts exhibiting shared behavior in online social media, particularly those exhibiting characteristics of automation and/or potential coordination. In addition, we develop edge weight variants of fuzzy competition graphs to further characterize common group behavior of automated accounts within subnetworks of social media ecosystems.
Funded by the Office of Naval Research.
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Overbey, L.A., Ek, B., Pinzhoffer, K., Williams, B. (2019). Using Common Enemy Graphs to Identify Communities of Coordinated Social Media Activity. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2019. Lecture Notes in Computer Science(), vol 11549. Springer, Cham. https://doi.org/10.1007/978-3-030-21741-9_10
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DOI: https://doi.org/10.1007/978-3-030-21741-9_10
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