Invited talk-1: Event detection from millions of tweets related to disasters using high-performance feature selection technique

Social media offers a wealth of insight into how significant events? such as the Great East Japan Earthquake, and the Nepal Earthquake affect individuals. The scale of available data, however, can be intimidating: during the Great East Japan Earthquake, over 8 million tweets were sent each day from Japan alone. Conventional word vector-based event-detection techniques for social media that use Latent Semantic Analysis, Latent Dirichlet Allocation, or graph community detection often cannot scale to such a large volume of data due to their space and time complexity. To alleviate this problem, we propose an efficient method for event detection by leveraging a fast feature selection algorithm called CWC. Our proposed method makes it possible to detect events from vast datasets. To demonstrate our method's effectiveness, we extract events from a dataset of over two hundred million tweets sent in the 21 days following the Great East Japan Earthquake and five million tweets sent in the 14 days after the Nepal Earthquake. With CWC, we can identify events from this dataset with great speewd and accuracy.