Spammer detection model based on hierarchical attention mechanism

doi:10.3969/j.issn.1000-1565.2023.01.014

Abstract

Abstract: The current network-based spammer detection methods only consider simple social relations and lack the utilization of more complex social semantic relations, so it is difficult to achieve optimal performance. Aiming at this challenge, this paper proposes a spammer detection model based on hierarchical attention mechanism(HAM-SD). The model first uses a heterogeneous information network to model social media to mine rich semantic and structural information, then uses a node-level attention layer to aggregate meta-path neighbors to enhance node representation, and uses an adaptive hierarchical aggregation module to select features at different levels to improve representation Then, the node representations under different meta-paths are fused through the semantic-level attention layer, and finally brought into the classification detection module to realize the detection of spammer. The results on public datasets show that the model can effectively detect spammer and maintain strong stability when the data distribution is unbalanced.

Key words: social media, spammer detection, heterogeneous information network, attention mechanism

CLC Number:

TP393

YANG Xiaohui, WANG Weibin. Spammer detection model based on hierarchical attention mechanism[J]. Journal of Hebei University(Natural Science Edition), 2023, 43(1): 95-102.

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