Mobile App for News Bias Detection Using Rule-Based Classification - Muhammad Rizqi Raka Siwi; Anita Fira Waluyo (Kirana Publisher (KNPub), 2026)
In the digital era, online media has become a primary source of information, but it also increases the risk of biased news dissemination that can influence public opinion. This study aims to develop a mobile application for automatically detecting bias in online news using web scraping and rule-based text classification. The application is built with Flutter and uses Firebase as the backend. The system retrieves articles from user-provided URLs and analyzes their content based on predefined keywords categorized into political, sensational, and confirmation bias. The results are presented as a bias score, label, and comparative analysis, and stored in user history. Black-box testing shows that all main features function as expected. The application is intended to support media literacy by helping users identify news bias independently.