We consider the following retweet prediction task: given a tweet, predict whether it will be retweeted. In  Copyright © 2016 Universitas Ahmad Dahlan. All rights reserved. Social media like Twitter has provided a platform for spreading information among users [1, 2] . In this work we focus on the retweet prediction problem. Given a tweet, we would like to predict whether it will be retweeted. Applications of this task are, for example, to help decision makers propagate their issues and facilitate companies to promote their products. A wide range of learning methods and features have been proposed for retweet prediction; see, e.g., [3] [4] [5] [6] [7] . In addition, different modeling setups have been proposed; e.g., Petrovic, et al., [3] propose a time-sensitive model that builds separate models depending on the tweet's creation time and show that it substantially improves performance over the passiveaggressive learning algorithm. This large variety of features, learning methods, and ways of modeling the task calls for a systematic comparison on a single dataset. Therefore, we propose a systematic comparison of the selected learning methods and features, both within and without time-sensitive framework, all on a single dataset. To the authors' knowledge this is the first attempt to collect best performing features and contrast linear and non-linear learning methods. We consider the following research questions: (i) Which of the proposed learning methods is the most effective for the retweet prediction task? (ii) Which of the proposed features are most discriminative features for the learning methods considered? And (iii) To which degree does time-sensitive modeling help improve the performance of learning methods on our dataset? Prior work has documented several techniques to solve the retweet prediction problem. Naveed, et al., [5] state that the problem of finding "interestingness" on Twitter is the same as predicting whether a tweet will be retweeted. They employ logistic regression to do the prediction and find that content features such as initial negative sentiments make a tweet more likely to be retweeted.
Bunyamin and Tunys REF , have provided a comparison of the performance for different learning methods and features, in terms of retweet prediction accuracy and feature importance, to understand what kind of tweets would be retweeted, by using as response variable a dummy variable representing the two states of being retweeted or not retweeted.
A Comparison of Retweet Prediction Approaches: The Superiority of Random Forest Learning Method
TELKOMNIKA Telecommunication Computing Electronics and Control
Computer Science