Paper: Identifying Event-related Bursts via Social Media Activities

ACL ID D12-1134
Title Identifying Event-related Bursts via Social Media Activities
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Activities on social media increase at a dra- matic rate. When an external event happens, there is a surge in the degree of activities re- lated to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore exhibit different bursty patterns. In this paper, we propose to iden- tify event-related bursts via social media activ- ities. We study how to correlate multiple types of activities to derive a global bursty pattern. To model smoothness of one state sequence, we propose a novel function which can cap- ture the state context. The experiments on a large Twitter dataset shows our methods are very effective.