Atchariyachanvanich, Kanokwan

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Atchariyachanvanich, Kanokwan
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kanokwan.at@kmitl.ac.th

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    Item type:Publication,
    Improvement of a Machine Learning Model Using a Sentiment Analysis Algorithm to Detect Fake News: A Case Study of Health and Medical Articles on Thai Language Websites
    (2024-01-01)
    Atchariyachanvanich, Kanokwan
    ;
    Saengkhunthod, Chotipong
    ;
    Kerdnoonwong, Parischaya
    ;
    Chanlekha, Hutchatai
    ;
    Cooharojananone, Nagul
    These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model’s prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.
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    Item type:Publication,
    Detection of Unreliable Medical Articles on Thai Websites
    (2021-01-21)
    Saengkhunthod, Chotipong
    ;
    Kerdnoonwong, Parischaya
    ;
    Atchariyachanvanich, Kanokwan
    Fake news have exerted terrible impact on the Thai society for a long time, especially fake health and medical news: unreliable news from social media have threatened people's mind and physical health. In this research, we investigated various methods for solving the problem of getting fake news on health and medical issues in social media. Then, we proposed to detect unreliable medical articles existed on Thai websites based on a machine learning. We collected samples of 297 reliable and 235 unreliable articles from 7 websites and analyzed the differences between them. Then, we selected 20 features that affected the reliability or unreliability of the articles and used machine learning to classify the articles according to those features. Experimental results show that XGBoost methods were the most effective at 90.60% accuracy.