  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.
