A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in twitter
Abstract
Twitter is a popular microblogging platform, which facilitates users to express views and thoughts on day-to-day events using short texts limited to a maximum of 280 characters. However, it is generally targeted by socialbots for political astroturfing, advertising, spamming, and other illicit activities due to its open and real-time information sharing and dissemination nature. In this paper, we present a socialbots analysis-driven graph-based approach for identifying coordinated campaigns among Twitter users. To this end, we first present statistical insights derived from the analysis of logged data of 98 socialbots, which were injected in Twitter and associated with top-six Twitter using countries. In the analysis, we study and present the impact of socialbots’ profile features, such as age and gender on infiltration. We also present a multi-attributed graph-based approach to model the profile attributes and interaction behavior of users as a similarity graph for identifying different groups of synchronized users involved in coordinated campaigns. The proposed approach is experimentally evaluated using four different evaluation parameters over a real dataset containing socialbots’ trapped user profiles. The evaluation of identified campaigns in the form of clusters reveals the traces of spammers, botnets, and other malicious users.
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The scalable and generalizable detection of coordination is still a largely open challenge, with only few contributions proposed so far.
“In fact, the scalable and generalizable detection of coordination is still a largely open challenge, with only few contributions proposed so far [12, 25].”
From A decade of social bot detection (2020) · cites this paper as [12]
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A decade of social bot detection
Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works
“In fact, the scalable and generalizable detection of coordination is still a largely open challenge, with only few contributions proposed so far [12, 25].”
BackgroundA language framework for modeling social media account behavior
Nwala, A. C., Flammini, A., & Menczer, F. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00410-9 · Cited by 21 · see citing works
- Authors
- Fazil, M., & Abulaish, M.
- Affiliations
- Jamia Millia Islamia (IN)
- South Asian University (IN)
- Corresponding author
- Abulaish, Muhammad
- Venue
- Journal of Intelligent & Fuzzy Systems
- Publisher
- SAGE Publications
- Published
- 2020-02-03
- Volume
- 38
- Issue
- 3
- Pages
- 2961-2977
- ISSN
- 1064-1246 (ISSN-L)
- 1875-8967
- OpenAlex
- W3004665981
- Cited by
- 14
- References
- 48
- Citations by year
- 2026: 2 · 2024: 3 · 2023: 3 · 2021: 3 · 2020: 3
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Intelligent & Fuzzy Systems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- social bots, Twitter, astroturfing, bot detection, graph clustering, similarity graph, user profile features, botnets, spam, malicious user detection
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