Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2012

Abstract

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 simultaneousy 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 event-driven 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 than an LDA baseline and two degenerate variations of our model. We also show some case studies that demonstrate the importance of considering both the temporal information and users’ personal interests for bursty topic detection from microblogs.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Publication

Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Jeju Island, Republic of Korea, 8-14 July 2012

First Page

536

Last Page

544

Publisher

ACL

City or Country

Stroudsburg, PA

Copyright Owner and License

LARC

Additional URL

http://www.aclweb.org/anthology/P12-1056

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