Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Series Vol. 52 , 27 March 2024


Open Access | Article

Designing a bias-rating news recommendation system

Siqian Liu * 1
1 Zhengzhou Fengyang Foreign Language School

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 52, 39-44
Published 27 March 2024. © 27 March 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Siqian Liu. Designing a bias-rating news recommendation system. ACE (2024) Vol. 52: 39-44. DOI: 10.54254/2755-2721/52/20241228.

Abstract

Media bias can significantly influence public perception, often subconsciously shaping opinions. To understand and measure this bias, diverse methodologies have emerged. While models from social sciences offer in-depth evaluations, they involve intensive manual analysis. In contrast, computerized models provide speed but often lack depth. This research explores the synergy between these disciplines, aiming to create a robust bias detection tool that combines the meticulousness of social science models with the automation of computer science. Using this interdisciplinary approach, a system was developed to evaluate articles and instantly present a 'bias score' on the user interface. This score offers readers an immediate indication of potential news slant. The research also integrated web crawling techniques into the system, allowing it to identify and recommend alternative articles on analogous subjects. This innovative feature enriches readers' choices, equipping them with multiple narratives for an enriched understanding. In conclusion, this work bridges the gap between depth and speed in media bias detection, offering a novel tool that promotes informed readership. The contribution of this study lies in its interdisciplinary approach and the development of a system that fosters holistic media consumption.

Keywords

Bias-Rating, Recommendation System, Political polarization

References

1. Bernhardt, D., Polborn, M., & Krasa, S. (2020). Political polarization and the electoral effects of media bias. CORE.

2. Qin, B., Stromberg, D., & Wu, Y. (2019). Media's Bias in China. American Economic Review, 108(9).

3. Keller, P. (2002). XIII Media Ownership and Regulation in China. Brill.

4. Digital media literacy. (2021, October 14). What is an echo chamber?

5. Debevere, P., Van Deursen, D., Van Rijsselbergen, D., & Van de Walle, R. (2010). Enabling Semantic Search in a News Production Environment. Research Gate.

6. United Nations General Assembly. (2022, August). Countering disinformation for the promotion and protection of human rights and fundamental freedoms.

7. Agrawal, S., & Goyal, N. (2013). Thompson sampling for contextual bandits with linear payoffs. International Conference on Machine Learning (pp. 127–135).

8. Adnan, M. N. M., Chowdury, M. R., Taz, I., & others. (2014). Content based news recommendation system based on fuzzy logic. Informatics, Electronics & Vision (ICIEV), 2014 International Conference on (pp. 1-6). IEEE.

9. Isidoro. (2013, February). Google’s Knowledge Graph: one step closer to the semantic web? Econsultancy.

10. Aggarwal, S., Sinha, T., Kukreti, Y., & Shikhar, S. (2022, September). Media bias detection and bias short term impact assessment. Science Direct.

11. Chiang, C-F., & Knight, B. (2011). Media Bias and Influence: Evidence from Newspaper Endorsements. Oxford University Press, 78(3).

12. Horne, B. D., Borit, N., Nevo, O’Donovan, J., Cho, J-H., & Adali, S. (2019, April 2). Rating Reliability and Bias in Articles: Does AI Assistance help everyone? International Conference on Web and Social Media.

13. Quijote, T. A., Zamoras, A. D., & Ceniza, A. (2019, March 11). Bias detection in Philippine political news articles using SentiWordNet and inverse reinforcement model. Semantic Scholar.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 4th International Conference on Signal Processing and Machine Learning
ISBN (Print)
978-1-83558-349-4
ISBN (Online)
978-1-83558-350-0
Published Date
27 March 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/52/20241228
Copyright
27 March 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated