Degeneracy-Based Real-Time Sub-Event Detection in Twitter Stream

In this paper, we deal with the task of sub-event de-tection in evolving events using posts collected from the Twitter stream. By representing a sequence of suc-cessive tweets in a short time interval as a weighted graph-of-words, we are able to identify the key mo-ments (sub-events) that compose an event using the con-cept of graph degeneracy. We then select a tweet to best describe each sub-event using a simple yet effec-tive heuristic. We evaluated our approach using human-generated summaries containing the actual important sub-events within each event and compare it to two baseline approaches using several performance metrics such as DET curves and precision/recall performance. Extensive experiments on recent sporting event streams indicate that our approach outperforms the dominant sub-event detection methods and constructs a human-readable event summary by aggregating the most repre-sentative tweets of each sub-event.