DETECTING UNSPECIFIED EVENTS ON TWITTER

Abstract

Twitter has become a popular platform among the available social media services for sharing opinions, experiences, news, and views in the real-time. It provides an exciting opportunity for detecting events happening around the world. Detecting events from Twitter data requires complex techniques. This research infers informational patterns about emerging events by characterizing the growth and divergence from Twitter data. We propose a novel graph-based approach, called the Dynamic Heartbeat Graph (DHG), which transforms the Twitter stream into temporal graphs. DHG suppresses dominating topics in the subsequent data stream, after their first detection. It measures the divergence in bursty topics and the cohesion in the topological structure of temporal graphs to detect events. The empirical evaluation of results on three well-known benchmark datasets (FA Cup, Super Tuesday, and the US Election) shows that the proposed DHG approach has superior performance and efficiency in comparison with the state-of-the-art approaches. The execution time analysis shows that the proposed approach is 47%, 64%, and 74% faster than the second-best approach used in the baselines. The proposed approach is implemented in the form of a research prototype that demonstrates the functionality and utility of an event detection system. In the end, the thesis concludes the study by reviewing the implications and accomplishments of the aims and objectives and presents future research directions.