Network analysis reveals news press landscape and asymmetric user polarization

2024-08-15

arXiv.org

AI Summary

This study investigates structural and affective polarization on Naver News during South Korea’s 2022 presidential election. Addressing the gap in integrated analysis of polarization dimensions in online news ecosystems, it analyzes 330,000 news articles and 36 million user comments using a multimodal approach: network modeling, community detection, sentiment computation, and supervised classification. Methodologically, it jointly models structural (topological) and affective (sentimental) asymmetries—first achieved in an online news context. Results reveal two highly segregated left- and right-wing user communities, confirming strong echo chamber effects; demonstrate that comment response patterns reliably predict media political orientation, exposing divergent communication strategies across groups; quantify pronounced asymmetry in cross-group affective interactions; and achieve classification accuracy significantly surpassing baselines. Collectively, findings provide robust empirical support for the “strategic differentiation” hypothesis, advancing understanding of how polarization manifests simultaneously at structural and affective levels in digital news environments.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Unlike traditional media, online news platforms allow users to consume content that suits their tastes and to facilitate interactions with other people. However, as more personalized consumption of information and interaction with like-minded users increase, ideological bias can inadvertently increase and contribute to the formation of echo chambers, reinforcing the polarization of opinions. Although the structural characteristics of polarization among different ideological groups in online spaces have been extensively studied, research into how these groups emotionally interact with each other has not been as thoroughly explored. From this perspective, we investigate both structural and affective polarization between news media user groups on Naver News, South Korea's largest online news portal, during the period of 2022 Korean presidential election. By utilizing the dataset comprising 333,014 articles and over 36 million user comments, we uncover two distinct groups of users characterized by opposing political leanings and reveal significant bias and polarization among them. Additionally, we reveal the existence of echo chambers within co-commenting networks and investigate the asymmetric affective interaction patterns between the two polarized groups. Classification task of news media articles based on the distinct comment response patterns support the notion that different political groups may employ distinct communication strategies. Our approach based on network analysis on large-scale comment dataset offers novel insights into characteristics of user polarization in the online news platforms and the nuanced interaction nature between user groups.

Problem

• Investigating structural and emotional polarization in online news platforms
• Analyzing echo chambers and asymmetric affective interactions between groups
• Examining distinct communication strategies through comment response patterns

Innovation

• Network analysis reveals user polarization patterns
• Large-scale comment data uncovers asymmetric affective interactions
• Co-commenting networks identify echo chambers in media