Document Type
Article
Department/Program
Data Science
Journal Title
EPJ Data Science
Pub Date
8-2023
Publisher
Springer
Volume
12
Issue
33
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
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.
Recommended Citation
Nwala, Alexander C.; Flammini, Alessandro; and Menczer, Filippo, A Language Framework for Modeling Social Media Account Behavior (2023). EPJ Data Science, 12(33).
https://doi.org/10.1140/epjds/s13688-023-00410-9
DOI
https://doi.org/10.1140/epjds/s13688-023-00410-9
