Temporal effects on hashtag reuse in Twitter: A cognitive-inspired hashtag recommendation approach

Dominik Kowald, Subhash Chandra Pujari, Elisabeth Lex

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

Abstract

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with the aim of designing a cognitive-inspired hashtag recommendation algorithm we call BLLI,S. Our main idea is to incorporate the effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags), and (ii) social hashtag reuse (i.e., reusing hashtags, which has been previously used by a followee) into a predictive model. For this, we turn to the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R, which accounts for the time-dependent decay of item exposure in human memory. We validate BLLI,S using two crawled Twitter datasets in two evaluation scenarios. Firstly, only temporal usage patterns of past hashtag assignments are utilized and secondly, these patterns are combined with a content-based analysis of the current tweet. In both evaluation scenarios, we find not only that temporal effects play an important role for both individual and social hashtag reuse but also that our BLLI,S approach provides significantly better prediction accuracy and ranking results than current state-of-the-art hashtag recommendation methods.

Original languageEnglish
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Pages1401-1410
Number of pages10
ISBN (Print)9781450349130
DOIs
Publication statusPublished - 1 Jan 2017
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017
CountryAustralia
CityPerth
Period3/04/177/04/17

Keywords

  • ACT-R
  • BLL equation
  • Hashtag recommendation
  • Hashtag reuse prediction
  • Hashtag usage recency
  • Hashtags
  • Recommender systems
  • Temporal dynamics
  • TF-IDF
  • Twitter

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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  • Cite this

    Kowald, D., Pujari, S. C., & Lex, E. (2017). Temporal effects on hashtag reuse in Twitter: A cognitive-inspired hashtag recommendation approach. In 26th International World Wide Web Conference, WWW 2017 (pp. 1401-1410). [3052605] International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3038912.3052605