article · Global Journal of Computer Science and Technology
The rapid circulation of human- and machine-generated fake news across social networks such as Facebook, X, and WhatsApp presents political and social difficulties for society and individuals. Prompt verification is challenging, creating a demand for automated fake news detection tools. Two data mining classification techniques, Extreme Gradient Boosting and Decision Tree, combined with Python features, were applied to classify news articles as real or fake. Training took place on a labelled dataset containing both genuine and fabricated news stories. Evaluation using standard metrics including accuracy, precision, recall, and F1-score demonstrated that the approach achieved 100 percent accuracy in separating real from fake news. These outcomes highlight the capability of data mining methods to counter misinformation and inform practitioners working in information verification and media literacy, with further testing planned on different datasets.
Misinformation spreads quickly across social media platforms, making manual verification difficult and creating social and political harm. Applying automated data mining techniques to identify fake news offers a scalable means to assess news reliability, supporting media literacy efforts and aiding professionals working in information verification to curb the distribution of fabricated content.
The approach could enable automated content-moderation and news-verification tools for social media networks, media literacy platforms, and fact-checking organisations. Because the system has only been tested on a single labelled dataset and still requires validation on different datasets, it remains early-stage research rather than a near-market commercial tool.
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Fake news becomes a major concern in the era of social media, as it can spread rapidly and has significant impacts on individuals and society.Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either generated by humans or machines. In the era of social networks such as Facebook, X (twitter) and WhatsApp, the quick rotation of fake news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issues, twodata mining classification techniques were used as Extreme Gradient Boosting and Decision Tree with some python features. This study is designed to use Decision Tree and Extreme Gradient Boosting methods to develop an effective approach for detecting and classifying news as real or fake to obtain a reliable model performance. These models are trained on a labeled dataset consisting of bothreal and fake news. The performance of the models was evaluated using standard evaluation metrics such as accuracy, precision, recall, and F1-score. The proposed approach achieved 100% accuracy in distinguishing between real and fake news. It revealed andhighlighted the potential of utilizing data mining techniques to combat the spread of fake news and provide valuable insights for researchers and practitioners in the field of information confirmation/verification and media literacy. We hope to use a different dataset to test the proposed model.
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DOI: 10.34257/gjcstcvol24is1pg1
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