基于混合图对比学习与强化增量聚类的无监督社交事件检测
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering
AI总结：
针对现有无监督社交事件检测方法存在的GCL信息学习不充分、依赖先验知识的问题，提出融合混合图对比学习与强化增量聚类的HCRC方法，在多个数据集和设置下实现显著性能提升。
摘要：
从社交媒体数据流中检测事件正逐渐吸引研究人员的关注。事件检测的固有挑战在于从社交媒体数据中提取判别性信息，进而将数据划分至不同事件。由于社交数据多样性过高且更新频率极快，采用监督方法从社交消息中检测事件难以实现。为此，近期研究探索了以无监督方式利用图对比学习（GCL）和嵌入聚类从社交消息中学习判别性信息。然而，基准方法存在两个内在问题：传统GCL仅能粗略探索部分属性，无法充分学习社交消息的判别性信息；基准方法需借助特定先验知识在潜在空间中对学习到的嵌入进行聚类，这与无监督学习范式的原则相悖。本文提出一种新型无监督社交媒体事件检测方法HCRC（混合图对比学习与强化增量聚类），该方法通过混合图对比学习全面学习社交消息中的语义和结构判别性信息，并采用强化增量聚类以完全无监督的方式实现高效聚类。我们在Twitter和Maven数据集上开展了全面实验以评估HCRC的性能。实验结果表明，该方法带来了持续显著的性能提升。在传统增量设置、半监督增量设置和完全无监督设置下，模型性能分别最高提升了53%、45%和37%。
英文摘要：
Detecting events from social media data streams is gradually attracting researchers. The innate challenge for detecting events is to extract discriminative information from social media data thereby assigning the data into different events. Due to the excessive diversity and high updating frequency of social data, using supervised approaches to detect events from social messages is hardly achieved. To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner. However, two intrinsic issues exist in benchmark methods: conventional GCL can only roughly explore partial attributes, thereby insufficiently learning the discriminative information of social messages; for benchmark methods, the learned embeddings are clustered in the latent space by taking advantage of certain specific prior knowledge, which conflicts with the principle of unsupervised learning paradigm. In this paper, we propose a novel unsupervised social media event detection method via hybrid graph contrastive learning and reinforced incremental clustering (HCRC), which uses hybrid graph contrastive learning to comprehensively learn semantic and structural discriminative information from social messages and reinforced incremental clustering to perform efficient clustering in a solidly unsupervised manner. We conduct comprehensive experiments to evaluate HCRC on the Twitter and Maven datasets. The experimental results demonstrate that our approach yields consistent significant performance boosts. In traditional incremental setting, semi-supervised incremental setting and solidly unsupervised setting, the model performance has achieved maximum improvements of 53%, 45%, and 37%, respectively.
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