Abstract
Microblogging platforms such as X and Mastadon have evolved into significant data sources, where the Hashtag Recommendation System (HRS) is being devised to automate the recommendation of hashtags for user queries. We propose a context-sensitive, Machine Learning based HRS named HaRNaT, that strategically leverages news articles to identify pertinent keywords and subjects related to a query. It interprets the fresh context of a query and tracks the evolving dynamics of hashtags to evaluate their relevance in the present context. In contrast to prior methods that primarily rely on microblog content for hashtag recommendation, HaRNaT mines contextually related microblogs and assesses the relevance of co-occurring hashtags with news information. To accomplish this, it evaluates hashtag features, including pertinence, popularity among users, and association with other hashtags. In performance evaluation of HaRNaT trained on these features demonstrates a macro-averaged precision of 84% with Naive Bayes and 80% with Logistic Regression. Compared to Hashtagify- a hashtag search engine, HaRNaT offers a dynamically evolving set of hashtags.
Published Version
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