Fake News Detection Using Web Scraping
Abstract
Fake news has become a major challenge in the digital age due to the swift distribution of information through social media and online platforms. The increasing volume of deceptive or inaccurate content can influence public opinion, create confusion, and damage trust in reliable sources. Traditional manual verification methods are time-consuming and inefficient when dealing with large volumes of data. This paper presents a Fake News Detection System that utilizes machine learning techniques to automatically classify news content as genuine or fake. The system analyzes textual features from news content through NLP-based methods and applies classification algorithms to determine credibility. By leveraging automated detection mechanisms, the system aims to improve information reliability and reduce the spread of misinformation across digital platforms.
References
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