A language framework for modeling social media account behavior

Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to tar...

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Bibliographic Details
Published in:EPJ data science 2023-12, Vol.12 (1), p.33-22, Article 33
Main Authors: Nwala, Alexander C., Flammini, Alessandro, Menczer, Filippo
Format: Article
Language:English
Subjects:
Automation
Bot detection
Complexity
Computer Appl. in Social and Behavioral Sciences
Computer Science
Coordination detection
Data science
Data-driven Science
Digital media
Elections
Encoding online behavior
Language
Modeling and Theory Building
Modelling
Neural networks
Regular Article
Social behavior
Social media
Social networks
User behavior
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Summary:Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of these methods decays as malicious behaviors evolve. To address this challenge, we propose a language framework for modeling social media account behaviors. Words in this framework, called BLOC, consist of symbols drawn from distinct alphabets representing user actions and content. Languages from the framework are highly flexible and can be applied to model a broad spectrum of legitimate and suspicious online behaviors without extensive fine-tuning. Using BLOC to represent the behaviors of Twitter accounts, we achieve performance comparable to or better than state-of-the-art methods in the detection of social bots and coordinated inauthentic behavior.
ISSN:2193-1127
2193-1127
DOI:10.1140/epjds/s13688-023-00410-9