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Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Author

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  • Areej Alsini

    (Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia
    Department of Computer Science, Umm Al-Qura University, Makkah 21995, Saudi Arabia)

  • Du Q. Huynh

    (Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia)

  • Amitava Datta

    (Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia)

Abstract

Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.

Suggested Citation

  • Areej Alsini & Du Q. Huynh & Amitava Datta, 2021. "Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review," Future Internet, MDPI, vol. 13(5), pages 1-19, May.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:5:p:129-:d:554465
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    References listed on IDEAS

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    1. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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    Cited by:

    1. Shangyi Yan & Jingya Wang & Zhiqiang Song, 2022. "Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

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