MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to eva...

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Bibliographic Details
Published inElectronics (Basel) Vol. 12; no. 10; p. 2298
Main Authors Zeng, Fanrui, Sun, Yingjie, Li, Yizhou
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 19.05.2023
Subjects
Analysis
Botnets
Classification
Coders
Control
Data security
Digital media
Elections
Graphical representations
Learning
Machine learning
Methods
Neural networks
Random walk
Social media
Social networks
User behavior
China
United States > US
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Summary:Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12102298