A document-level model for tweet event detection

Social media like Twitter who serves as a novel news medium and has become increasingly popular since its establishment. Large scale first-hand user-generated tweets motivate automatic event detection on Twitter. Previous unsupervised approaches detected events by clustering words. These methods detect events using burstiness, which measures surging frequencies of words at certain time windows. However, event clusters represented by a set of individual words are difficult to understand. This issue is addressed by building a document-level event detection model that directly calculates the burstiness of tweets, leveraging distributed word representations for modeling semantic information, thereby avoiding sparsity. Results show that the document-level model not only offers event summaries that are directly human-readable, but also gives significantly improved accuracies compared to previous methods on unsupervised tweet event detection, which are based on words/segments.