Finding Bursty Topics from Microblogs

Microblogs such as Twitter reflect the general public’s reactions to major events. Bursty topics 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 topics that have bursty patterns on microblogs, we propose a topic model that simultaneously captures two observations: (1) posts published around the same time are more likely to have the same topic, and (2) posts published by the same user are more likely to have the same topic. The former helps find eventdriven posts while the latter helps identify and filter out “personal” posts. Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model...

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Qiming Diao, Jing Jiang, Feida Zhu, Ee-Peng Lim
• Keywords: ACL 2012, Computational Linguistics, Personal Interests, Personal Posts, Text Streams