An Entity-Centric Real-Time Event Detection Framework for Thai Social Media Using Multi-Granularity TCC-Aware Named Entity Recognition

Real-time event detection from social media has become increasingly important for emergency response, public safety, and situational awareness. However, accurately identifying emerging events from Thai social media remains challenging because Thai is a low-resource language without explicit word boundaries, while social media text is often characterized by informal writing, spelling variations, and noisy user-generated content. This paper proposes an entity-centric real-time event detection framework for Thai social media that employs a Multi-Granularity Thai Character Cluster (TCC)-Aware Named Entity Recognition (NER) model as its core information extraction component. The proposed NER architecture integrates contextual word embeddings, character-level representations, Thai Character Cluster features, and Part-of-Speech embeddings through an attention-based feature fusion mechanism to improve entity recognition under noisy conditions. Recognized entities are subsequently used as semantic anchors for event construction, clustering, temporal trend analysis, event ranking, and alert generation within a unified streaming framework. Event discovery combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Exponential Moving Average (EMA)-based temporal analysis to identify emerging events in real time. Experiments conducted on a large-scale Thai social media corpus demonstrate that the proposed model achieves an F1-score of 94.14% for named entity recognition and 92.2% for downstream event detection, outperforming representative baseline methods. Additional ablation studies, qualitative error analysis, and statistical significance tests confirm the effectiveness of the proposed multi-granularity representation. These results demonstrate that the proposed framework provides an effective solution for real-time event monitoring in low-resource language environments.