Title: Recommendation of hashtags in social Twitter network

Authors: King Chau Cheung; Tommy King Yin Cheung

Addresses: Department of Statistics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong ' Department of Supply Chain and Information Management, Hang Seng Management College, Shatin, Hong Kong

Abstract: The development of microblogging services has resulted in the growth of short-text social networking on the internet which open the door to many useful applications such as reputation management and marketing. With more than millions of tweets generated each day, Twitter is one of the largest microblogging sites which allow users to use hashtags to categorise and facilitate the search of tweets which share the same tag. By using a popular or appropriate hashtag in tweets, users could reach a large set of target followers. In this paper, we propose a novel hidden topic model for content-based hashtag recommendation. By ranking the occurrence probability of hashtags of a given topic, a set of hashtag candidates was selected for further analysis. The proposed method is demonstrated with tweets collected from Twitter's API for 19 consecutive periods. The advantage of our model is a combination of the use of topic distribution and term selection probability for hashtag recommendation.

Keywords: hashtag recommendation; topic models; Twitter; short-text classification.

DOI: 10.1504/IJDATS.2017.086631

International Journal of Data Analysis Techniques and Strategies, 2017 Vol.9 No.3, pp.222 - 236

Received: 08 Dec 2015
Accepted: 28 Mar 2016
Published online: 15 Sep 2017 *

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