Infact: Intelligent Fake News & Sentiment Analysis

Abstract

The blistering development of digital journalism and social media has contributed to the proliferation of misinformation and fake news [1], which affects the overall discourse, ruins the reputations of individuals and even can affect the election results [3]. Manual methods of fact checking are not scalable and are also time consuming and traditional [4]. To solve this, we suggest INFACT (Intelligent Fake News and Sentiment Analysis Framework), a modular system where machine learning is applied [6], and natural language processing,sentiment analysis [5], and credibility scoring to detect, classify, and analyze fake news in real time. The framework integrates six interconnected modules: web scraping, fake news detection, multimodal analysis, sentiment analysis, credibility scoring, and trend tracking. Implemented using Python, Django, and scikit learn, the system aims to provide users with reliable classification, interpretability through sentiment and credibility scores, and visual insights into misinformation trends.