Fake News Detection and Classification to Counter Misinformation in Online Social Networks Using Natural Language Processing

In the age of technological advancement, computers analyze user content on social networks, where fake news significantly influences opinions, business intelligence, social dynamics, and IoT applications. This research examines fraudulent content in textual communications from online social networks, namely Twitter, to improve the precision of fake news identification. This study looks at machine learning methods, including Support Vector Machine, Bernoulli Naïve Bayes, and Logistic Regression, to see how well they improve the identification of context in text. Unlike current techniques that often overlook these steps, the proposed methodology integrates feature extraction and selection, resulting in enhanced accuracy across all Twitter datasets. This detailed approach offers important results for researchers and professionals looking to understand context from user-created content, showing useful applications in real-life situations like opinion polls and business analysis.