This project focuses on developing a browser extension to enhance users’ news literacy by helping them identify potentially manipulative or morally framed content in online news articles. The main objective of the project is to explore how automated analysis techniques can help users critically evaluate how news stories are presented on digital platforms. The proposed system analyzes the textual elements of news articles and identifies linguistic patterns associated with emotional language, exaggeration, and moral framing. By examining the wording in news content, the extension attempts to detect signals of persuasive or emotionally loaded framing strategies. These indicators are then presented to the user in an accessible and interpretable way. The system is implemented as a browser extension that processes news articles directly on the webpages visited. When a user accesses an online news article, the extension analyzes the text and highlights relevant indicators of framing or manipulation. The results of this analysis are presented via an interactive interface integrated into the browser. The goal of the project is to investigate how lightweight automated text analysis tools can help readers reflect on the framing and presentation of online news content, ultimately contributing to improved critical reading and digital news literacy.