Profiling Astroturfing Facebook Users During Three Contiguous Israeli Election Periods

Schler, Jonathan; Bonchek-Dokow, Elisheva; Vainstein, Tomer; Gotam, Moshe; Teplitsky, Mike

doi:10.1109/bigdata50022.2020.9378207

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2024

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Cited by 3 publications

References 17 publications

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Securing social spaces: machine learning techniques for fake profile detection on instagram

Goyal,

Gill,

Gulia

2024

Soc. Netw. Anal. Min.

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Securing social spaces: machine learning techniques for fake profile detection on instagram

Goyal,

Gill,

Gulia

2024

Soc. Netw. Anal. Min.

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Investigating Astroturfing Detection: An Overview of its Domains, Mitigation Techniques and Future Perspectives

Badade,

Kumar Dhanaraj

2024

2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies

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Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification

Schler,

Bonchek-Dokow

2024

MAKE

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The practice of online astroturfing has become increasingly pervasive in recent years, with the growth in popularity of social media. Astroturfing consists of promoting social, political, or other agendas in a non-transparent or deceitful way, where the promoters masquerade as normative users while acting behind a mask that conceals their true identity, and at times that they are not human. In politics, astroturfing is currently considered one of the most severe online threats to democracy. The ability to automatically identify astroturfers thus constitutes a first step in eradicating this threat. We present a complete framework for handling a dataset of profiles, from data collection and efficient labeling, through feature extraction, and finally, to the identification of astroturfers lurking in the dataset. The data were collected over a period of 15 months, during which three consecutive elections were held in Israel. These raw data are unique in scope and size, consisting of several million public comments and reactions to posts on political candidates’ pages. For the manual labeling stage, we present a technique that can zoom in on a sufficiently large subset of astroturfer profiles, thus making the procedure highly efficient. The feature extraction stage consists of a temporal layer of features, which proves useful for identifying astroturfers. We then applied and compared several algorithms in the classification stage, and achieved improved results, with an F1 score of 77% and accuracy of 92%.

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Profiling Astroturfing Facebook Users During Three Contiguous Israeli Election Periods

Cited by 3 publications

References 17 publications

Securing social spaces: machine learning techniques for fake profile detection on instagram

Securing social spaces: machine learning techniques for fake profile detection on instagram

Investigating Astroturfing Detection: An Overview of its Domains, Mitigation Techniques and Future Perspectives

Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and Classification

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