Hashtag-based topic evolution in social media

Md Hijbul Alam, Woo Jong Ryu, Sang-Geun Lee

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1527-1549
Number of pages23
JournalWorld Wide Web
Issue number6
Publication statusPublished - 2017 Nov 1

Bibliographical note

Publisher Copyright:
© 2017, Springer Science+Business Media New York.


  • Hashtag distribution
  • Social media
  • Topic evolution
  • Topic model

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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