Unsupervised Social Bot Detection via Structural Information Theory
ACM Transactions on Information Systems(2024)
摘要
Research on social bot detection plays a crucial role in maintaining the
order and reliability of information dissemination while increasing trust in
social interactions. The current mainstream social bot detection models rely on
black-box neural network technology, e.g., Graph Neural Network, Transformer,
etc., which lacks interpretability. In this work, we present UnDBot, a novel
unsupervised, interpretable, yet effective and practical framework for
detecting social bots. This framework is built upon structural information
theory. We begin by designing three social relationship metrics that capture
various aspects of social bot behaviors: Posting Type Distribution, Posting
Influence, and Follow-to-follower Ratio. Three new relationships are utilized
to construct a new, unified, and weighted social multi-relational graph, aiming
to model the relevance of social user behaviors and discover long-distance
correlations between users. Second, we introduce a novel method for optimizing
heterogeneous structural entropy. This method involves the personalized
aggregation of edge information from the social multi-relational graph to
generate a two-dimensional encoding tree. The heterogeneous structural entropy
facilitates decoding of the substantial structure of the social bots network
and enables hierarchical clustering of social bots. Thirdly, a new community
labeling method is presented to distinguish social bot communities by computing
the user's stationary distribution, measuring user contributions to network
structure, and counting the intensity of user aggregation within the community.
Compared with ten representative social bot detection approaches, comprehensive
experiments demonstrate the advantages of effectiveness and interpretability of
UnDBot on four real social network datasets.
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