Prediction of Retweets Based on User, Content, and Time Features Using
EUSBoost

Prediction of Retweets Based on User, Content, and Time Features Using
EUSBoost

: information diffusion, retweet prediction, EUSBoost

Twitter is one of the popular microblogs that allow users to write posts. Retweeting is one of the mechanisms for the diffusion
of information on Twitter. One way to understand the spread of information is to learn about retweet predictions. This study
focuses on predicting retweets using Evolutionary Undersampling Boosting (EUSBoost) based on user, content, and timebased features. We also consider the vector of text as a predictive feature. Models with EUSBoost are able to outperform
models using the AdaBoost method. The evaluation results show that the best model can achieve an AUC performance score
of 77.21% and a GM score of 77.18%. While the Adaboost-based models achieved AUC scores ranging from 68% to 69% and
GM scores ranging from 62% to 63%. In addition, we found that there was no significant difference between using numeric
features only and combining numeric and text features

Ghina Khoerunnisa1
, Jondri2
, Widi Astuti3

School of Computing, Telkom University

30-06-2022

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