Bursty event detection from microblog: a distributed and incremental approach

As a new form of social media, microblogs (e.g., Twitter and Weibo) are playing an important role in people's daily life. With the rise in popularity and size of microblogs, there is a need for distributed approaches that can detect bursty event with low latency from the short-text data stream. In this paper, we propose a distributed and incremental temporal topic model for microblogs called Bursty Event dEtection (BEE+). BEE+ is able to detect bursty events from short-text dataset and model the temporal information. And BEE+ processes the post-stream incrementally to track the topic drifting of events over time. Therefore, the latent semantic indices are preserved from one time period to the next. In order to achieve real-time processing, we design a distributed execution framework based on Spark engine. To verify its ability to detect bursty event, we conduct experiments on a Weibo dataset of 6,360,125 posts. The results show that BEE+ can outperform the baselines for detecting the meaningful bursty events and track the topic drifting. Copyright (C) 2015 John Wiley & Sons, Ltd.