Arabic Fake News Detection using Ensemble Technique

Similar to most spoken languages worldwide, Arabic language is facing a significant problem with the rapid spread of fake information, which requires effective methods to detect and combat it. This research introduces a novel approach to detecting fake news in Arabic using Ensemble Techniques. The process involves gathering a comprehensive collection of Arabic news articles and carefully labeling them as either fake or authentic. To ensure the quality of the data, various text preprocessing techniques such as cleaning, tokenization, and stemming are applied. The detection accuracy is then enhanced by leveraging Ensemble Techniques. Multiple base classifiers, each with its own strengths, are employed to thoroughly analyze the text data for deceptive patterns. By utilizing this ensemble approach, the diversity of classifiers is harnessed to improve the reliability of detecting fake news. In this case, a robust ensemble learning framework that combines three states of the art algorithms are utilized for the detection of fake news. Two different methods for extracting features, Term Frequency and Term Frequency-Inverse Document Frequency are used to improve the accuracy of identifying fake news. The results show significant improvements in fake news detection accuracy, which provide valuable insights and methodologies for combating misinformation in the Arabic digital sphere, which is crucial for maintaining information integrity and promoting informed decision-making.