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Research topics and trends of the hashtag recommendation domain

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Abstract

In microblogging platforms, hashtags are used to annotate the microblogs for a more convenient categorization and analysis of the published contents. Due to the fast growth of the social network, the hashtag recommendation field has attracted the researchers’ attention most recently. In this study, a review of existing works in the hashtag recommendation filed is presented. After collecting all the papers in this field, the author keywords are exploited in order to extract popular topics and explore the evolution of them since their inception. In this regard, statistical analysis of the keywords, keyword-pairs co-occurrences, and the cluster analysis through the co-word data (co-word analysis) are performed. The obtained results demonstrate that there are four evolved thematic areas in this research field, including “SIMILARITY”, “HASHTAG-RECOMMENDATION”, “MACHINE-LEARNING”, and “POPULARITY-PREDICTION”. Besides, there are some popular themes in each thematic area, such as the “DEEP_LEARNING”, which has excellent future development potential. Similarly, the “SIMILARITY” and “TOPIC-MODEL” are two motor themes that have gained increased interest from researchers in recent studies. Eventually, the analysis results of the related works in the hashtag recommendation domain are utilized to extract the main approaches in this research area involving “DEEP LEARNING”, “TOPIC MODELING”, “SIMILARITY”, “CLASSIFICATION”, and “TOPICAL TRANSLATION”. The results’ implications and the future research directions determined that the researchers’ interest in the field of hashtag recommendation will increase rapidly.

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Authors and Affiliations

  1. School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Babak Amiri, Ramin Karimianghadim & Navid Yazdanjue

  2. Ron and Carol Cope Professor and Department Chair, Cyber Systems, University of Nebraska, Kearney, NE, USA

    Liaquat Hossain

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  2. Ramin Karimianghadim
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  3. Navid Yazdanjue
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  4. Liaquat Hossain
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Correspondence to Babak Amiri.

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Appendix A: Core documents

Appendix A: Core documents

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Table 10 The core documents of each theme inside the 2011–2019 period
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10

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Amiri, B., Karimianghadim, R., Yazdanjue, N. et al. Research topics and trends of the hashtag recommendation domain. Scientometrics 126, 2689–2735 (2021). https://doi.org/10.1007/s11192-021-03874-6

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