Unmasking Disinformation: Detection of Fake News Online Using Learning Techniques

In an era where fake news proliferates effortlessly across social media platforms, the task of discerning reliable news sources has become increasingly challenging. Detecting fake news in the realm of social media presents unique characteristics and complexities that render traditional detection algorithms ineffective. Recent research endeavors have sought to address these challenges by leveraging a diverse array of methods, including machine learning, deep learning, feature engineering and graph mining. In our research, we investigate the detection of fake news by employing a combination of traditional machine learning algorithms and deep learning models for fake news classification. Furthermore, we extend our research by conducting real-time analysis for fake news detection using Twitter data and data from fact-checking sites. By combining the strengths of traditional machine learning and deep learning, our research offers a holistic approach to fake news detection, enhancing the accuracy and robustness of our results.