Hashtag-based topic evolution in social media
- Authors
- Alam, Md Hijbul; Ryu, Woo-Jong; Lee, SangKeun
- Issue Date
- 11월-2017
- Publisher
- SPRINGER
- Keywords
- Topic evolution; Hashtag distribution; Topic model; Social media
- Citation
- WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, v.20, no.6, pp.1527 - 1549
- Indexed
- SCIE
SCOPUS
- Journal Title
- WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
- Volume
- 20
- Number
- 6
- Start Page
- 1527
- End Page
- 1549
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/81773
- DOI
- 10.1007/s11280-017-0451-3
- ISSN
- 1386-145X
- Abstract
- The rise of online social media has led to an explosion of metadata-containing user generated content. The tracking of metadata distribution is essential to understand social media. This paper presents two statistical models that detect interpretable topics over time along with their hashtags distribution. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags, i.e., the hashtag distribution. The models combine a context with a related topic by jointly modeling words with hashtags and time. Experiments with real-world datasets demonstrate that the proposed models discover topics over time with related contexts effectively.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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