PureFeed: A Machine Learning-Driven News Aggregator for Unbiased and Sensationalism-Free Journalism

Authors

  • Vedant Paun Department of Computer Science & Engineering, MIT ADT University, Pune, Maharashtra, India Author
  • Lakshya Porohit Department of Computer Science & Engineering, MIT ADT University, Pune, Maharashtra, India Author
  • Shubham Somani Department of Computer Science & Engineering, MIT ADT University, Pune, Maharashtra, India Author

DOI:

https://doi.org/10.32628/IJSRSET2512326

Keywords:

Unbiased news, click-bait detection, media bias, news app, natural language processing, machine learning, Pure Feed, MongoDB, custom API, misinformation

Abstract

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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References

Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., & Nakov, P. (2020). What was said versus who said it: Analyzing the effects of news labels on readers. In Proceedings of the 28th International Conference on Computational Linguistics.

Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. science, 359(6380), 1146-1151.

Vayadande, K., Kale, D. R., Nalavade, J., Kumar, R., & Magar, H. D. (2024). Text generation & classification in nlp: A review. How Machine Learning is Innovating Today's World: A Concise Technical Guide, 25-36.

de Mendonça, M. P. C., Moraes, I. M., & Mattos, D. M. F. (2025). Automatic Inference of Brazilian Websites' Reliability for Combating Fake News: Domain and Geolocation Features. Journal of Internet Services and Applications, 16(1), 43-53.

Munger, A. L., Speirs, K. E., Grutzmacher, S. K., & Edwards, M. (2024). Social service providers’ perceptions of older adults’ food access during COVID-19. Journal of Aging & Social Policy, 36(6), 1244-1261.

Newman, J., Flower, L., Jones, R., Phillips, V., Couturier, D. L., Law, M., ... & Summers, C. (2025). Longer term post-hospital morbidity and mortality following admission with COVID-19: a systematic review and meta-analysis. ERJ Open Research.

Kale, D. R., Buchade, A., Nalavade, J., Sapate, S. G., & Umbarkar, A. J. (2023, October). Detecting Violations of Conditional Functional Dependencies in Distributed Database. In 2023 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) (pp. 1-4). IEEE

Kale, D. R., & Aparadh, S. Y. (2016). A Study of a detection and elimination of data inconsistency in data integration. International Journal of Scientific Research in Science, Engineering and Technology, 2(1), 532-535

Baly, R., Martino, G. D. S., Glass, J., & Nakov, P. (2020). We can detect your bias: Predicting the political ideology of news articles. arXiv preprint arXiv:2010.05338.

Kale, D. R., & Mulla, J. M. S. (2024). AI in healthcare: Enhancing patient outcomes through predictive analytics. Industrial Engineering Journal, 53(5), 73-79. Industrial Engineering Journal

Chakraborty, R., Deka, M., & Sarma, S. K. (2024). Syntactic Category based Assamese Question Pattern Extraction using N-grams. Procedia Computer Science, 235, 214-230.

Potthast, M., Köpsel, S., Stein, B., & Hagen, M. (2016). Clickbait detection. In Advances in Information Retrieval: 38th European Conference on IR Research, ECIR 2016, Padua, Italy, March 20–23, 2016. Proceedings 38 (pp. 810-817). Springer International Publishing.

Rubin, V. L., Conroy, N., Chen, Y., & Cornwell, S. (2016, June). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection (pp. 7-17).

Yin, H., Cui, B., Chen, L., Hu, Z., & Huang, Z. (2014, June). A temporal context-aware model for user behavior modeling in social media systems. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (pp. 1543-1554).

Kale, D. R., Nalvade, J., Randive, P. S., & Hirve, S. (2024). Artificial Intelligence in Sustainable Agriculture: Enhancing Efficiency and Reducing Environmental Impact. ARTIFICIAL INTELLIGENCE, 53(5).

Thuseethan, S., Janarthan, S., Rajasegarar, S., Kumari, P., & Yearwood, J. (2020, December). Multimodal deep learning framework for sentiment analysis from text-image web data. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (pp. 267-274). IEEE.

Nalavade, J. E., Sachdeo, R., Kale, D. R., Buchade, A., Subhedar, M., & Shinde, S. K. (2024, December). Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks. In 2024 IEEE Pune Section International Conference (PuneCon) (pp. 1-6). IEEE

Liu, X., Liu, H., & Ding, C. (2013, June). Incorporating user behavior patterns to discover workflow models from event logs. In 2013 IEEE 20th International Conference on Web Services (pp. 171-178). IEEE.

Kale, M. D. R., & Todmal, M. S. R. (2015). A Result Paper on Investigation of Incremental Detection Problems in Distributed Data. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(12).

BOUAOUINA Rania Sirine, S. B. (2024). The Impact of Biased American Media on Public Perception and Political Decision Making.

Kale, D. R., Jadhav, A. N., Salunkhe, S. J., Hirve, S., & Goswami, C. (2024, October). Sharding: A Scalability Solutions for Blockchain Networks. In 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS) (pp. 1-8). IEEE

Kale, D. R., Mane, T. S., Buchade, A., Patel, P. B., Wadhwa, L. K., & Pawar, R. G. (2024, October). Federated Learning for Privacy-Preserving Data Mining. In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA) (pp. 1-6). IEEE

Kale, D. R., & Todmal, S. R. (2014). A survey on big data mining applications and different challenges. Int J Adv Res Comput Eng Technol, 3, 3835-3838.

Dattatray Raghunath Kale. (2024). Quantum Machine Learning Algorithms for Optimization Problems: Theory, Implementation, and Applications. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 322–331. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6218

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Published

15-05-2025

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Section

Research Articles

How to Cite

[1]
Vedant Paun, Lakshya Porohit, and Shubham Somani, “PureFeed: A Machine Learning-Driven News Aggregator for Unbiased and Sensationalism-Free Journalism”, Int J Sci Res Sci Eng Technol, vol. 12, no. 3, pp. 175–182, May 2025, doi: 10.32628/IJSRSET2512326.

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