Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

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 article, 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.

Automated summary

Events happening in the real world and real time can be planned for various occasions. Social media platforms provide real-time text information on public events, which are challenging to mine due to their heterogeneous nature. This article proposes a novel event-based meta-schema and a Pairwise Popularity Graph Convolutional Network (PP-GCN) for social event categorization. Additionally, a streaming social event detection and evolution discovery framework based on meta-path similarity search is introduced, outperforming alternative techniques in experiments on real-world social text data.