Fake News Detection on Social Media using SVM

Misinformation has emerged as a major concern on social media, resulting in confusion among the public and manipulation of opinions. This initiative seeks to create an automated system for detecting fake news employing the Support Vector Machine (SVM) algorithm to categorize news articles as either "Fake" or "Real." The system handles text data by cleaning, tokenizing, and extracting significant features using CountVectorizer and TF-IDF, which convert textual material into numerical formats appropriate for machine learning models. The SVM classifier is trained on a labeled dataset that includes both fake and real news articles, allowing it to recognize patterns and connections between word frequencies and their probability of suggesting false information. In contrast to conventional keyword-based detection techniques that depend on specific words and phrases, often leading to a high rate of false positives and negatives, this method leverages machine learning to enhance precision and adaptability. Furthermore, elements like sentiment analysis and user credibility are included to boost detection effectiveness. The developed model is implemented as a web-based application utilizing Flask, enabling users to submit news articles for instant fake news evaluation. This system tackles the drawbacks of manual fact-checking, which can be tedious and laborintensive, and offers a scalable, automated solution for spotting misinformation. Unlike deep learning models that demand large labeled datasets and substantial computational resources, SVM provides an efficient and effective classification approach suited for real-time applications.