  Events are happening in real-world and real-time, which can be planned and
organized occasions involving multiple people and objects. Social media
platforms publish a lot of text messages containing public events with
comprehensive topics. However, mining social events is challenging due to the
heterogeneous event elements in texts and explicit and implicit social network
structures. In this paper, we design an event meta-schema to characterize the
semantic relatedness of social events and build an event-based heterogeneous
information network (HIN) integrating information from external knowledge base,
and propose a novel Pair-wise Popularity Graph Convolutional Network (PP-GCN)
based fine-grained social event categorization model. We propose a
Knowledgeable meta-paths Instances based social Event Similarity (KIES) between
events and build a weighted adjacent matrix as input to the PP-GCN model.
Comprehensive experiments on real data collections are conducted to compare
various social event detection and clustering tasks. Experimental results
demonstrate that our proposed framework outperforms other alternative social
event categorization techniques.
