The rapid spread of fake news across digital platforms has posed significant challenges to information integrity and public trust. Detecting fake news effectively requires robust methods that go beyond superficial content analysis. This study presents a novel approach for fake news detection using supervised learning combined with Natural Language Processing (NLP) techniques, specifically focusing attribution. on in-article The method leverages a combination of textual features, sentiment analysis, and linguistic cues to identify discrepancies in article authorship, credibility, and factuality. By analyzing both the content and the inherent attribution of the article itself, the model can determine whether a news piece aligns with known sources or shows signs of manipulation