Mobile App for News Bias Detection Using Rule-Based Classification

Muhammad Rizqi Raka Siwi; Anita Fira Waluyo

doi:10.58526/jsret.v5i2.1122

Authors

Muhammad Rizqi Raka Siwi Universitas Teknologi Yogyakarta
Anita Fira Waluyo Universitas Teknologi Yogyakarta

DOI:

https://doi.org/10.58526/jsret.v5i2.1122

Keywords:

Bias Detection, Online News, Web Scraping, Rule-Based Classification, Mobile Application, Media Literacy

Abstract

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.

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