TY - CHAP
T1 - Feature-Driven Method for Identifying Pathogenic Social Media Accounts
AU - Alvari, Hamidreza
AU - Shaabani, Elham
AU - Shakarian, Paulo
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85101124034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101124034&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-61431-7_7
DO - 10.1007/978-3-030-61431-7_7
M3 - Chapter
AN - SCOPUS:85101124034
T3 - SpringerBriefs in Computer Science
SP - 77
EP - 94
BT - SpringerBriefs in Computer Science
PB - Springer
ER -