A Hierarchical Framework for Fake News Detection Using Semantic Analysis and Source Reliability

Fake news has become a significant problem in modern society, with the rapid dissemination of false information through social media and online platforms. This has led to an urgent need for effective methods to detect and combat fake news. In this paper, we propose a hierarchical framework for fake news detection using semantic analysis and source reliability. The framework comprises two main tiers: semantic analysis and source reliability. In the first stage, the text of the news article is analyzed using natural language processing techniques to extract features such as keywords, sentiment, and tone. Similarly, the credibility of the source is determined based on factors such as the website's domain authority and previous track record of spreading fake news. In the second tier, a hierarchical classifier combines the results from the semantic analysis and source reliability levels to make the final prediction. The hierarchical classifier initially employs a binary classifier to determine if the article is fake or not. If it is classified as fake, it is then passed on to another classifier to determine the severity of the fake news. Our framework aims to provide a more accurate and robust approach for detecting fake news by considering both the content of the news article and the credibility of its source. Experimental results on a real-world dataset demonstrate the effectiveness of our proposed framework compared to traditional fake news detection techniques.