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Polarized collaboration benefits knowledge production: empirical analyses of the mediating effect of co-production pattern in Wikipedia articles on climate change

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  • Kunhao Yang

    (Yamaguchi University)

  • Mengyuan Fu

    (Waseda University)

Abstract

The global influence of climate change has become increasingly noticeable during recent years. A deeper understanding of human collective behavior’s role in mitigating climate change necessitates the provision of high-quality knowledge to the public. In the modern society, the Internet has emerged as the primary knowledge source, raising concerns about the quality of information provided by online communities. Understanding the mechanisms of online knowledge co-production is crucial for enhancing information quality. Prior research has highlighted the substantial influence of group-level political polarization on online knowledge production, though there is disagreement about whether its impact is positive or negative. This study proposed the co-production pattern, which reflects how participants with differing political preferences collaborated with each other, as a mediator and analyzed its impacts on co-production process of climate change knowledge in Wikipedia. To this end, the research amassed two datasets including over 1.3 million entries documenting editing behaviors in English-language articles on climate change hosted on Wikipedia, encompassing nearly ten thousand Wikipedia editor teams. The results empirically demonstrated the positive impacts of polarized teams in preventing vandalism and boosting reliability when knowledge co-production occurs between participants with different political viewpoints. These insights suggest strategies for effectively producing and improving climate change knowledge by leveraging polarized online communities.

Suggested Citation

  • Kunhao Yang & Mengyuan Fu, 2024. "Polarized collaboration benefits knowledge production: empirical analyses of the mediating effect of co-production pattern in Wikipedia articles on climate change," Journal of Computational Social Science, Springer, vol. 7(3), pages 2677-2699, December.
    • Handle: RePEc:spr:jcsosc:v:7:y:2024:i:3:d:10.1007_s42001-024-00321-3
    DOI: 10.1007/s42001-024-00321-3
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