Events are happening in real-world and real-time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this paper, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.
翻译:现实世界和实时活动正在发生,可以在社交集会、节日庆祝活动、有影响力的会议或体育活动等场合规划和组织活动; 社交媒体平台产生大量关于不同主题的公共活动的实时文本信息; 然而,采矿社会事件具有挑战性,因为事件通常表现出不同质素和元数据。 在本文中,我们首先设计一个基于事件的新元系统,以描述社会事件的语义关联性,然后建立一个基于事件的综合信息网络(HIN),将来自外部知识库的信息整合在一起; 其次,我们提议建立一个名为PPPP-P-GCN的新型PAirwith Paily Popicalal Convolutionalalal 网络,以加权的元病原样实例相似性为名,以文字表达文字表达作为投入,进行细微的社会事件分类,并学习不同任务中元病原体的最佳重量。 第三,我们提议基于元病类搜索、关于元病历的历史信息以及混杂的DBSCDAN组合方法,为HINs设计一个流社会事件探测和进化发现框架。 有关现实世界探索事件的综合实验,以其他社会发现方法展示了我们的社会数据,以其他社会变异变变。