Ensemble Machine Learning for Identifying Fake News Headlines in Thailand
Main Article Content
Abstract
Advancements in information technology (IT) have rapidly transformed news dissemination across on- line platforms, creating challenges in analyzing di- verse, often non-credible news sources. This issue is especially notable in Thai headlines due to linguis- tic nuances and broad topic range. Consequently, individuals are susceptible to the dissemination of false information, resulting in emotional, financial, and stability damages. To address this concern, this research proposes the utilization of term frequency- inverse document frequency (TF-IDF) in conjunc- tion with various machine learning (ML) models as a multi-classifier, i.e., multinomial naïve Bayes (MNB), k-nearest neighbor (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost) al- gorithms. All classifier models can classify fake news from the news headlines within the Thai context. To enhance the classification accuracy, ensemble ML techniques, such as fuzzy integral and blending, were applied. A dataset comprising news headlines from the Anti-Fake News Center Thailand, under the Min- istry of Digital Economy and Society, is constructed for analysis. The proposed ensemble blending tech- nique with logistic regression demonstrates a com- mendable accuracy rate of up to 97%, underscoring its efficacy in distinguishing authentic news from mis- information.
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