I know your stance! Analyzing Twitter users' political stance on diverse perspectives

Abstract

The popularity of social network service users has increased in recent years, altering politicians' interest level in social network services. Given this trend, social network services now play a central role in political communication channels, enabling them to express and share their opinions and news directly with citizens. Therefore, many researchers have attempted to investigate social network service users' political stances and proposed a user's political stance model utilizing this dataset. Understanding and detecting social network services and a user's political stance can play a significant role in marketing strategies and determining election winners. In light of this, the present study examined Twitter from diverse perspectives to analyze and detect a Twitter user's political stance. This study collected Twitter datasets and labeled a user's stance using a clustering approach to determine whether a user was a Democrat or a Republican. After an exploratory analysis of users' tweet content: image and text, user network, and profile description, the tweet user stance detection model was proposed and tested. The results indicated notable differences between Democrats and Republicans from diverse perspectives on Twitter, with an accuracy of 85.35% compared with baseline models. The implications and limitations of this study were discussed based on the results.