Paper: Finding Bursty Topics from Microblogs

ACL ID P12-1056
Title Finding Bursty Topics from Microblogs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012

Microblogs such as Twitter reflect the general public?s reactions to major events. Bursty top- ics from microblogs reveal what events have attracted the most online attention. Although bursty event detection from text streams has been studied before, previous work may not be suitable for microblogs because compared with other text streams such as news articles and scientific publications, microblog posts are particularly diverse and noisy. To find top- ics that have bursty patterns on microblogs, we propose a topic model that simultaneous- ly captures two observations: (1) posts pub- lished around the same time are more like- ly to have the same topic, and (2) posts pub- lished by the same user are more likely to have the same topic. The former helps find event- driven posts while the latt...