Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Areej Alsini,Du Q Huynh,Amitava Datta

doi:10.3390/fi13050129

Areej Alsini, Du Q Huynh + Show 1 more

Open Access

https://doi.org/10.3390/fi13050129

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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.

Highlights

We propose an alternative way of classifying hashtag recommendation methods
This paper discusses how hashtag recommendation systems for tweets have evolved within the field of online social networks
It presents a new taxonomy for hashtag recommendation of tweets based on their methodologies

Summary

Introduction

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. We search for articles related to our research between 2010 and 2020 from. 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. 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.

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