Recommendation of hashtags in social Twitter network Online publication date: Fri, 15-Sep-2017
by King Chau Cheung; Tommy King Yin Cheung
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 9, No. 3, 2017
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook