FAKE NEWS DETECTION USING SENTIMENT ANALYSIS AND MACHINE LEARNING ALGORITHMS

Resumen

Nowadays, huge volumes of fake news are continuously posted by many users with fraudulent goals thus leading to very negative social effects on individuals and society and causing continuous threats to democracy, justice, and public trust. Fake news detection is a really challenging in today’s world, and it has become more extensive and harder to identify. A major challenge in fake news detection is the unavailability of labelled data for training the classification models. This paper provided a standard framework in order to analyze and classify the twitter data by the most widely used feature extraction and machine learning techniques for fake news detection. Experiments conducted on well-known and widely used real-world datasets show advantages and drawbacks in terms of accuracy and efficiency for the considered approaches, even in the case of limited dataset. Various performance metrics are applied for experimental comparison and verification to verify the effectiveness of the method.