SSKG: Subject stream knowledge graph, a new approach for event detection from text

Events play a crucial role in shaping societal meaning and discourse. They are recorded in text data streams from social networks or online sources and analyzing them is vital for predicting and managing future occurrences. Methods for event detection from text data streams have been developed to enhance accuracy and efficiency in analyzing large datasets. This study focuses on events published as text data streams in Telegram. Two main perspectives are considered for better results: firstly, major events can overshadow the detection of smaller ones with fewer narrative instances. Secondly, due to diverse narratives surrounding each event, establishing meaningful connections between narrative sets is essential. In this research, two new concepts, "subject stream" and "stream graph," are introduced. The subject stream aims to process topics to mitigate the impact of bursty events on detecting others. The stream graph models data stream and identifies various narratives associated with each event for better identification and categorization. Combining these approaches accurately represents events and their characteristics in the real world. Implementing the system in three versions demonstrates the effectiveness of using the "stream graph" alongside the "subject stream," resulting in improved execution speed and accuracy. Evaluation results show a 6% enhancement in topic recall.