Prediction of Retweets Based on User, Content, and Time Features Using
EUSBoost
Dublin Core
Title
Prediction of Retweets Based on User, Content, and Time Features Using
EUSBoost
EUSBoost
Subject
: information diffusion, retweet prediction, EUSBoost
Description
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
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
Creator
Ghina Khoerunnisa1
, Jondri2
, Widi Astuti3
, Jondri2
, Widi Astuti3
Publisher
School of Computing, Telkom University
Date
30-06-2022
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Ghina Khoerunnisa1
, Jondri2
, Widi Astuti3, “Prediction of Retweets Based on User, Content, and Time Features Using
EUSBoost,” Repository Horizon University Indonesia, accessed June 27, 2025, https://repository.horizon.ac.id/items/show/9188.
EUSBoost,” Repository Horizon University Indonesia, accessed June 27, 2025, https://repository.horizon.ac.id/items/show/9188.