A Framework for Classifying Indonesian News Curator in Twitter
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
re-tweet feature. However, there are limited prior works for classifying
Indonesian News Curator in twitter and still hard for labeling 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 analyzing the influential features of certain class, so
it’s make easier for labeling 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 re-tweet. 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.
Author: Jaka E. Sembodo, Erwin
B. Setiawan, ZKA Baizal
Journal Code: jptkomputergg170145
