Open AccessUnderstanding Political Polarization Based on User Activity: A Case Study in Korean Political YouTube Channels
Author(s) -
Giang T.C. Tran,
Luong Vuong Nguyen,
Jason J. Jung,
Jin-Suk Han
Publication year - 2022
Publication title -
sage open
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.357
H-Index - 32
ISSN - 2158-2440
DOI - 10.1177/21582440221094587
Subject(s) - polarization (electrochemistry) , data collection , computer science , enthusiasm , politics , world wide web , information retrieval , statistics , political science , psychology , mathematics , social psychology , chemistry , law
This study proposes a novel approach for measuring political polarization using a user-activity-based model. By exploiting data from comments, user activity in this study is defined based on features such as coverage, duration, and enthusiasm. To determine these features, we collect information on the activities of users from South Korean YouTube channels. Notably, the collected data of the model contains approximately 11 M comments from more than 600 K users based on 37 K videos of 77 YouTube channels. To handle the big data collection, we deploy a web-based platform called TubePlunger to collect video information (e.g., comments, replies, etc.) automatically from YouTube channels. The output of the model reveals that the users are strongly polarized because the number of neutral users is very small (approximately 8% of the total). We then applied this model to the other channels in the testing dataset to define polarization with a bias percentage and to visualize the user activity distribution. The experimental results show that there are 30 fully polarized YouTube channels (16 left-wing channels and 14 right-wing channels) with a measured bias ratio higher than 70%. Our method of analyzing social network data based on user activity provides the foundation for polarization analysis that can be applied to fields other than politics.