Bursty Event Detection via Multichannel Feature Alignment.

To quickly identify bursty events that are emerging and developing in their early stages is important for our emergency response and public security. Daily news and social media are two major channels for people to contact with the world, thus become the main sources for the bursty event detection. However, recent works either use daily news only which is authoritative and well-organized, but easily out of date, or use social media only which is real-time and abundant, but contain a lot of noise. In this paper, to construct an efficient and effective bursty event detection system, we propose to combine the data from daily news and social media channels. Firstly, bursty features are extracted from social media and initially grouped into bursty events. Then, the data from two channels are aligned by using supervised learning at the level of events. Finally, we use the news to verify the detected result from social media by alignment algorithms. Experimental results show that our framework outperforms baselines.