An Ensemble Machine Learning Approach for Reliable Fake News Detection on Social Media Platforms
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P116Keywords:
Fake News Detection, social media, Machine Learning, Text Classification, ROC-AUC, Class Imbalance, Machine learningAbstract
The high rates at which such misinformation is spread on social media platforms have further increased the need to have a strong and automated fake news detection system. This paper uses the Fake News Net dataset, which combines news and social characteristics, to offer a workable machine learning (ML)-based method for detecting bogus news. The first step in the methodology consists of a thorough text preprocessing, such as tokenization, lemmatization, noise, normalization of cases, and elimination of stop words, to ensure high-quality input to model training. After the cleaned dataset has been further divided into train and test subsets, the two classification models, Light Gradient Boosting Machine (Light GBM) and Decision Tree (DT), are used. Light GBM has been proven to be better as it can reliably deal with high volumes of textual data, potentially involving intricate patterns in context and is fast thanks to its scalable nature and leaf-wise tree growing approach. Experimental investigations show that Light GBM achieves an accuracy score of 86.02, outperforming the Decision Tree model and popular models like Random Forest and Naive Bayes. Furthermore, performance measurements including as accuracy, recall, F1-score, confusion matrices, and ROC curves confirm the reliability of the proposed technique. The comparative analysis justifies that Light GBM has a greater generalized and stable classification ability. All things considered, the essay presents a practical and successful method for identifying fake news, which raises the material's trustworthiness and stops false information from spreading on social media.
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