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Jaka E. Sembodo
Telkom University
Computer Science & IT

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A Framework for Classifying Indonesian News Curator in Twitter Jaka E. Sembodo; Erwin B. Setiawan; ZK A. Baizal
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.4557

Abstract

News curators in twitter are a user, which is interested in following, spreading, giving feedback of recent popular articles. There are two kinds of this user, news curator as human user and news aggregator as bot user. In prior works about news curator, the classification system built using followers, URL, mention and retweet feature. However, there are limited prior works for classifiying Indonesian News Curator in twitter and still hard for labelling data involve just two features: followers and URL. In this paper, we proposed a framework for classifying Indonesian news curator in twitter using Naïve Bayes Classifier (NBC) and added features such as location, bio profile, and common tweet. Another purpose for analysing the influential features of certain class, so its make easier for labelling data of this role in the future. Examination result using percentage split as evaluating system produced 87% accuracy. The most influential features for news curator are followers, bio profile, mention and retweet. For news aggregator class are followers, location, and URL. The rest just common tweet feature for not both class. We implemented Feature Subset Selection (FSS) for increasing system performance and avoiding the over fitting data, it has produced 92.90% accuracy.
Co-Authors Erwin B. Setiawan ZK Abdurahman Baizal