基于持续事件知识网络的持续社会事件分类研究

doi:10.11896/jsjkx.240600146

Abstract

Abstract: With the rapid development of the Internet and the burgeoning scale of social media,social event classification(SEC) has garnered increasing attention.The existing study of SEC focuses on recognizing a fixed set of social events.However,in real-world scenarios,new social events continually emerge on social media,which suggests the necessity for a practical SEC model that can swiftly adapt to the evolving environment with incremental social events.Therefore,this paper studies a new yet crucial pro-blem defined as continual social event classification(C-SEC),where new events continually emerge in the sequentially collected social data.Accordingly,this paper proposes a novel continual event knowledge network(CEKNet) to continually learn continual event knowledge for C-SEC with continually incremental events.The proposed continual learning framework consists of two components:present event knowledge learning and past event knowledge replay.Firstly,it conducts present event knowledge learning to learn the classification of newly emerging events in the presently incoming data.Secondly,it designs past event knowledge replay with self-knowledge distillation to consolidate the learned knowledge of past events and prevent catastrophic forgetting.Comprehensive experiments on real-world social event datasets demonstrate the superiority of the proposed CEKNet for C-SEC compared with state-of-the-art methods.

Key words: Social event classification, Class-incremental continual learning, Continual event knowledge

CLC Number:

TP391

ZHANG Yuan, ZHANG Shengjie, LIU Lilong, QIAN Shengsheng. Research on Continual Social Event Classification Based on Continual Event Knowledge Network[J].Computer Science, 2025, 52(8): 268-276.

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