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Network analysis reveals news press landscape and asymmetric user polarization

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  • Lee, Byunghwee
  • Ryu, Hyo-sun
  • Lee, Jae Kook
  • Jeong, Hawoong
  • Kim, Beom Jun

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.

Suggested Citation

  • Lee, Byunghwee & Ryu, Hyo-sun & Lee, Jae Kook & Jeong, Hawoong & Kim, Beom Jun, 2025. "Network analysis reveals news press landscape and asymmetric user polarization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 676(C).
    • Handle: RePEc:eee:phsmap:v:676:y:2025:i:c:s0378437125004947
    DOI: 10.1016/j.physa.2025.130842
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    References listed on IDEAS

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    1. Ronald E. Robertson & Jon Green & Damian J. Ruck & Katherine Ognyanova & Christo Wilson & David Lazer, 2023. "Users choose to engage with more partisan news than they are exposed to on Google Search," Nature, Nature, vol. 618(7964), pages 342-348, June.
    2. Li, Xianghua & Wang, Zhen & Gao, Chao & Shi, Lei, 2017. "Reasoning human emotional responses from large-scale social and public media," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 182-193.
    3. repec:plo:pone00:0074516 is not listed on IDEAS
    4. Chao Gao & Jiming Liu, 2015. "Uncovering Spatiotemporal Characteristics of Human Online Behaviors during Extreme Events," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-14, October.
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