Political Bias Detection in News using Deep Learning
DOI:
https://doi.org/10.32628/CSEIT25112866Abstract
Traditional sentiment analysis typically uses abstract labels like 'positive,' 'negative,' or 'neutral.' However, for political news articles, it is crucial to capture more nuanced information that represents the specific political orientation of the content. In this paper, we categorize political bias into three labels: Left, Center, and Right to provide a more ideological understanding of the bias present in news articles. We utilize a deep learning model to classify news articles based on political ideological bias. Various methods and techniques are employed to enhance the model's efficiency and performance, ultimately improving its ability to provide a clearer, more detailed representation of the political stance in news content. This will result in a more lucid understanding of the news articles on different types of political ideological biases subtly used in writing the news articles.
Downloads
References
Bird S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly Media, Inc.
Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14(3), 130–137. https://doi.org/10.1108/eb046814
Jeffrey Pennington, Richard Socher, and Christopher Manning. (2014). GloVe: Global Vectors for Word Representation. EMNLP, 1532–1543.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735
K. Cho et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. arXiv preprint arXiv:1406.1078
Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of Reporting and Bias of News Media Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3528–3539, Brussels, Belgium. Association for Computational Linguistics.
M. Arkajyoti and B. Sanjib. “Political Bias Analysis.” Stanford University Computer Science, 2016.
Zhi-Hua Zhou, “Ensemble Methods: Foundations and Algorithms,” CRC Press, 2012.
M. Santana, All The News: Bias Dataset, Kaggle, 2020. [Online]. Available: https://www.kaggle.com/datasets/mayobanexsantana/political-bias
J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, 1977. ISBN: 978-0201076165.
H. He and E. A. Garcia, "Learning from Imbalanced Data," in IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, Sept. 2009, doi: 10.1109/TKDE.2008.239.
L. Prechelt, “Early stopping - but when?” in Neural Networks: Tricks of the Trade, Springer, Berlin, Heidelberg, 1998, pp. 55–69. doi: 10.1007/3-540-49430-8_3
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.