Research topics and trends of the hashtag recommendation domain

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.