Abstract
In this chapter, we present a feature-driven approach to detect PSM accounts in social media. Inspired by the literature, we set out to assess PSMs from three broad perspectives: (1) user-related information (e.g., user activity, profile characteristics), (2) source-related information (i.e., information linked via URLs shared by users) and (3) content-related information (e.g., tweets characteristics). For the user-related information, we investigate malicious signals using causality analysis (i.e., if user is frequently a cause of viral cascades) and profile characteristics (e.g., number of followers, etc.). For the source-related information, we explore various malicious properties linked to URLs (e.g., URL address, content of the associated website, etc.). Finally, for the content-related information, we examine attributes (e.g., number of hashtags, suspicious hashtags, etc.) from tweets posted by users. Experiments on real-world Twitter data from different countries demonstrate the effectiveness of the proposed approach in identifying PSM users.
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Notes
- 1.
In this work we used the following: https://voiceofeurope.com/, https://newsvoice.se/, https://nyadagbladet.se/, https://www.friatider.se/, or the pro-Russian website https://ok.ru/.
- 2.
A codebook is survey research approach to provide a guide for framing categories and coding responses to the categories definitions.
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Alvari, H., Shaabani, E., Shakarian, P. (2021). Feature-Driven Method for Identifying Pathogenic Social Media Accounts. In: Identification of Pathogenic Social Media Accounts. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-61431-7_7
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