Real-Time Entity-Based Event Detection for Twitter

In recent years there has been a surge of interest in using Twitter to detect real-world events. However, many state-of-the-art event detection approaches are either too slow for real-time application, or can detect only specific types of events effectively. We examine the role of named entities and use them to enhance event detection. Specifically, we use a clustering technique which partitions documents based upon the entities they contain, and burst detection and cluster selection techniques to extract clusters related to on-going real-world events. We evaluate our approach on a large-scale corpus of 120 million tweets covering more than 500 events, and show that it is able to detect significantly more events than current state-of-the-art approaches whilst also improving precision and retaining low computational complexity. We find that nouns and verbs play different roles in event detection and that the use of hashtags and retweets lead to a decreases in effectiveness when using our entity-base approach.