Hashtag sense induction based on co-occurrence graphs

Mengmeng Wang, Mizuho Iwaihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Twitter hashtags are used to categorize tweets for improving search categorizing topic. But the fact that people can create and use hashtags freely leads to a situation such that one hashtag may have multiple senses. In this paper, we propose a method to induce senses of a hashtag in a particular time frame. Our assumption is that for a sense of a hashtag the context words around it are similar. Then we design a method that uses a co-occurrence graph and community detection algorithm. Both words and hashtags are nodes of the cooccurrence graph, and an edge represents the relation of two nodes co-occurring in the same tweet. A list of words with a high node degree representing a sense is extracted as a community of the graph. We take Wikipedia disambiguation list page as word sense inventory to refine the results by removing non-sense topics.

Original languageEnglish
Title of host publicationWeb Technologies and Applications - 17th Asia-PacificWeb Conference,APWeb 2015, Proceedings
EditorsReynold Cheng, Bin Cui, Zhenjie Zhang, Ruichu Cai, Jia Xu
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319252544
Publication statusPublished - 2015
Event17th Asia-PacificWeb Conference, APWeb 2015 - Guangzhou, China
Duration: 2015 Sep 182015 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th Asia-PacificWeb Conference, APWeb 2015


  • Co-occurrence Graph
  • Hashtag
  • Sense induction
  • Twitter
  • Wikipedia

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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