International Journal of Engineering Technology and Management Sciences

2023, Volume 7 Issue 4

Detection of Partisan Bias in Political Social Media Posts using Naïve Bayes Algorithm

AUTHOR(S)

Arun Padmanabhan, Dr. Devasenapathy. K

DOI: https://doi.org/10.46647/ijetms.2023.v07i04.090

ABSTRACT
This research study investigates the detection of partisan bias in political social media posts through the application of the Naive Bayes algorithm. The CrowdFlower Political Social Media Posts dataset is utilized, comprising a collection of labelled posts from diverse political affiliations. The primary objective of this research is to develop an automated system that can effectively classify political posts based on their partisan biases. The study employs data pre-processing techniques, feature extraction methods, and the Naive Bayes algorithm to evaluate the performance of this approach. The findings of this research showcase the potential for accurate detection of partisan bias, contributing to a deeper understanding of political discourse on social media platforms. In order to achieve the research objectives, the study begins by exploring the prevalence of partisan bias in political discussions on social media and the subsequent influence on public opinion. A comprehensive review of text classification algorithms is conducted, highlighting the effectiveness and suitability of the Naive Bayes algorithm for this particular task. The research methodology encompasses multiple stages, including data pre-processing to standardize the text data, feature extraction using the bag-of-words approach, and training a classification model with the Naive Bayes algorithm. The model's performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.

Page No: 647 - 652

References:

1  Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends® in information retrieval, 2(1-2), 1-135.
2  Chen, D., &Lerman, K. (2020). Political hashtag hijacking in the 2016 US election. arXiv preprint arXiv:2001.01224.
3  Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.
4  McLaughlin, A., Osborne, M., & Bates, J. (2021). Analyzing partisan bias in political news articles using machine learning techniques. In 2021 IEEE 19th International Conference on Software Engineering Research, Management and Applications (SERA) (pp. 105-112). IEEE.
5  Bakliwal, A., Garg, S., & Sudhakar, A. (2012). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 47(12), 16-24.
6  Hutto, C. J., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International Conference on Weblogs and Social Media (ICWSM-14).
7  Seo, J., & Ginsberg, J. (2019). Understanding partisan audience dynamics on Twitter: A natural experiment during the 2016 presidential election. Social Media+ Society, 5(1), 2056305119831727.
8  Yang, B., & Counts, S. (2010). Predicting the speed, scale, and range of information diffusion in twitter. In Fourth International AAAI Conference on Weblogs and Social Media.
9  Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In ICML (Vol. 3, pp. 616-623).
10   Chen, Z., Wei, F., & Wang, Z. (2017). Understanding partisan bias in social media platforms: A case study of Weibo. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 2311-2314).
11   Chen, T., &Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
12   Rajagopalan, S., & Agarwal, S. (2018). Analyzing political bias in news articles. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 681-688). IEEE.
13   Bird, S., Klein, E., &Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. O'Reilly Media Inc.
14   Chen, Z., Wei, F., & Wang, Z. (2017). Understanding partisan bias in social media platforms: A case study of Weibo. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 2311-2314).
15   Liu, K., Wu, S., Fu, R., & Zhang, Y. (2019). A comparative study of machine learning approaches for political tweet sentiment analysis. Concurrency and Computation: Practice and Experience.
16  A.J Sriganapathy; S Sudhan; T. Sundarmagalingam; E. Nijilabinash; T. Siva. "Design and Analysis of Vertical Axis Wind Turbine Blade". International Research Journal on Advanced Science Hub, 2, 5, 2020, 8-20. doi: 10.47392/irjash.2020.26
17  R Soundararajan; V Hariprasath; R Keerthivasan; S Muthukumar; C Naveenkumar. "Experimental Investigation of EDM Process Parameters on Aluminum Nano composite using Taguchi Technique". International Research Journal on Advanced Science Hub, 2, 5, 2020, 1-7. doi: 10.47392/irjash.2020.25
18   A.J Sriganapathy; R Gunasekaran; N T. Kalaiarasan; R Balabharathi; T Haridas. "Optimization in Micro aerial Vehicle for Higher Performance". International Research Journal on Advanced Science Hub, 2, 5, 2020, 21-26. doi: 10.47392/irjash.2020.27
19   Karthikeyan A G; Pradeep V P; Maruthapandian .. "Optimization of Multiple Input Process Parameters of WEDM on Titanium Ti 6Al-4v (Grade 5) By Taguchi Analysis". International Research Journal on Advanced Science Hub, 2, 6, 2020, 1-10. doi: 10.47392/irjash.2020.29
20   Shubhangi Taneja. "Implementing the Digital Learning". International Research Journal on Advanced Science Hub, 2, 6, 2020, 72-74. doi: 10.47392/irjash.2020.39
21    Jaspreet Singh; Charanjit Singh. "Multilevel Space-Time Codes on Hoyt Fading Channel". International Research Journal on Advanced Science Hub, 2, 6, 2020, 75-78. doi: 10.47392/irjash.2020.40
22    Amandeep Singh; Charanjit Singh. "Security Tools for Internet of Things: A Review". International Research Journal on Advanced Science Hub, 2, 6, 2020, 87-91. doi: 10.47392/irjash.2020.42
23   Mohd. Akbar; Prasadu Peddi; Balachandrudu K E. "Inauguration in Development for Data Deduplication Under Neural Network Circumstances". International Research Journal on Advanced Science Hub, 2, 6, 2020, 154-156. doi: 10.47392/irjash.2020.5


  • How to Cite This Article:
    Arun Padmanabhan, Dr. Devasenapathy. K . Detection of Partisan Bias in Political Social Media Posts using Naïve Bayes Algorithm . ijetms;7(4):647-652. DOI: 10.46647/ijetms.2023.v07i04.090