Bursty event detection on social media

Messages posted on social media such as Twitter and Instagram are a rich and promising source of information on real-life events. However, due to the high volume and the noisy nature of posts on social media, the messages reporting events are usually overwhelmed by unrelated daily chatter. To detect unspecified events, many topic modeling and wavelet signal processing methods have been proposed. In this paper, we propose an improved method, BCCED, using a burstiness index and co-occurrence clustering for event detection, that builds on the Event Detection with Clustering of Wavelet-based Signals (EDCoW) method of Weng et al. [2011]. We compare their performance with two topic modeling methods on two social media datasets. Experiments show that BCCED outperforms these alternatives for unspecified event detection from social media.