Identifying relevant event content for real-time event detection

A variety of event detection algorithms for microblog services have been proposed, but their accuracy relies on the microblog feeds they analyse. Existing research explores datasets that are collected using either a set of manually predefined terms or information from external sources. These methods fail to provide comprehensive and quality feeds for real-time event detection. In this paper, we present a novel adaptive keyword identification approach to retrieve a greater amount of event relevant content. This approach continuously monitors emerging hashtags and rates them by their similarity to specific pre-defined event hashtags using TF-IDF vectors. Top rated emerging hashtags are added as filter criteria in real time. By comparing our proposed approach, called CETRe (Content-based Event Tweet Retrieval) with an existing baseline approach applied to real-world events, we show that CETRe not only identifies event topics and contents, but also enables better event detection.