PureFeed: A Machine Learning-Driven News Aggregator for Unbiased and Sensationalism-Free Journalism
DOI:
https://doi.org/10.32628/IJSRSET2512326Keywords:
Unbiased news, click-bait detection, media bias, news app, natural language processing, machine learning, Pure Feed, MongoDB, custom API, misinformationAbstract
In an era of widespread misinformation, sensationalism, and biased reporting, the need for trustworthy and objective news delivery has become increasingly critical. This paper presents Pure Feed, a news aggregation application designed to provide users with unbiased, click bait-free content across all major news categories. The app utilizes a custom-built API and MongoDB for efficient storage and retrieval of news articles and integrates a machine learning model trained to detect and filter out bias and sensationalism using natural language processing techniques. By analyzing linguistic patterns, tone, and sentiment, Pure Feed ensures that the information presented is factual, balanced, and free from editorial manipulation. The goal is to restore public trust in digital news media and offer a cleaner, more transparent user experience. The paper discusses the design, implementation, and effectiveness of the bias detection model, as well as the overall system architecture and impact potential of Pure Feed in the current media landscape.
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