Event detection on social platforms can help people perceive essential events and make actionable decisions. Existing document-pivot streaming social event detection methods generally embed documents and perform text clustering. They face the challenges of constantly changing context and unknown event categories and struggle by designing compound text representation methods and various similarity measures. However, phased, well-designed methods are excessively fragile and unable to utilize the potential of text representations fully. Meanwhile, their complex threshold settings result in clustering-based event detection suffering the pain of ever-changing environments. We propose a text representation learning method namely Text Similarity Contrastive Learning Neural Network (Text-SimCLNN) to tackle these challenges. Text-SimCLNN uses smaller parts to learn the similarity probability of text pairs from semantic and structural perspectives, effectively bridging the gap between text representation learning and similarity measure in streaming event detection. Event discovery and merging in streams can be easily performed based on the learned representations, and we use various techniques to speed up such processes. Furthermore, we introduce an online update mechanism that uses heterogeneous graphs to generate high-quality samples to enable stable and reliable inductive learning. Extensive experiments on two real-world datasets demonstrate that our method far exceeds state-of-the-art (SOTA).

The prevalent heterogeneous Graph Neural Network (GNN) models learn node and graph representations using pre-defined meta-paths or only automatically discovering meta-paths. However, the existing methods suffer from information loss due to neglecting undiscovered meta-structures with richer semantics than meta-paths in heterogeneous graphs. To take advantage of the current rich meta-structures in heterogeneous graphs, we propose a novel approach called HeGTM to automatically extract essential meta-structures (i.e., meta-paths and meta-graphs) from heterogeneous graphs. The discovered meta-structures can capture more prosperous relations between different types of nodes that can help the model to learn representations. Furthermore, we apply the proposed approach for text classification. Specifically, we first design a heterogeneous graph for the text corpus, and then apply HeGTM on the constructed text graph to learn better text representations that contain various semantic relations. In addition, our approach can also be used as a strong meta-structure extractor for other GNN models. In other words, the auto-discovered meta-structures can replace the pre-defined meta-paths. The experimental results on text classification demonstrate the effectiveness of our approach to automatically extracting informative meta-structures from heterogeneous graphs and its usefulness in acting as a meta-structure extractor for boosting other GNN models.