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

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.