TY - GEN
T1 - Call of Duty or Call of Dependency
T2 - 6th International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024
AU - Benitez-Orellana, Leonardo
AU - Recalde, Lorena
AU - Loza-Aguirre, Edison
AU - Ramírez-Cifuentes, Diana
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Video game addiction is an emerging global concern with significant impacts on individual well-being. This study develops a machine learning-based framework to detect potential addiction traits using data from Reddit, where 987 user posts were analyzed. The goal was to characterize signs of video game addiction and develop predictive models. Various text representation and word embedding techniques, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2vec, and BERT, were employed alongside analyses of emotional expression and topical content in the posts. These posts were preliminarily labeled by psychologists to facilitate the training of models. Four supervised classification algorithms—Logistic Regression, KNN, Decision Tree, and AdaBoost—were utilized for model evaluation. The study highlights the efficacy of embedding-based models, particularly the combination of Word2vec and Logistic Regression, which achieved the highest accuracy of 0.94. These findings advance our understanding of how machine learning can be leveraged to identify behavioral patterns associated with video game addiction in social media contexts, pointing towards potential applications in clinical and psychological diagnostics.
AB - Video game addiction is an emerging global concern with significant impacts on individual well-being. This study develops a machine learning-based framework to detect potential addiction traits using data from Reddit, where 987 user posts were analyzed. The goal was to characterize signs of video game addiction and develop predictive models. Various text representation and word embedding techniques, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2vec, and BERT, were employed alongside analyses of emotional expression and topical content in the posts. These posts were preliminarily labeled by psychologists to facilitate the training of models. Four supervised classification algorithms—Logistic Regression, KNN, Decision Tree, and AdaBoost—were utilized for model evaluation. The study highlights the efficacy of embedding-based models, particularly the combination of Word2vec and Logistic Regression, which achieved the highest accuracy of 0.94. These findings advance our understanding of how machine learning can be leveraged to identify behavioral patterns associated with video game addiction in social media contexts, pointing towards potential applications in clinical and psychological diagnostics.
KW - Machine learning
KW - Mental disorders
KW - Reddit
KW - Text modeling
KW - Video game addiction
UR - https://www.scopus.com/pages/publications/105030227410
U2 - 10.1007/978-3-031-98768-7_3
DO - 10.1007/978-3-031-98768-7_3
M3 - Contribución a la conferencia
AN - SCOPUS:105030227410
SN - 9783031987670
T3 - Lecture Notes in Networks and Systems
SP - 35
EP - 51
BT - Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024 - Volume 1
A2 - Garcia, Marcelo V.
A2 - Reyes, John-Paul
A2 - Nuñez, Carlos
A2 - Gordón-Gallegos, Carlos
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 21 October 2024 through 25 October 2024
ER -