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Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations

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  • Gallus, Jana
  • Bhatia, Sudeep

Abstract

This paper studies the conversations behind the operations of a large-scale, online knowledge production community: Wikipedia. We investigate gender differences in the conversational styles (emotionality) and conversational domain choices (controversiality and gender stereotypicality of content) among contributors, and how these differences change as we look up the organizational hierarchy. In the general population of contributors, we expect and find significant gender differences, whereby comments and statements from women are higher-valenced, have more affective content, and are in domains that are less controversial and more female-typed. Importantly, these differences diminish or disappear among people in positions of power: female authorities converge to the behavior of their male counterparts, such that the gender gaps in valence and willingness to converse on controversial content disappear. We find greater sorting into topics according to their gender stereotypicality. We discuss mechanisms and implications for research on gender differences, leadership behavior, and conversational phenomena arising from such large-scale forms of knowledge production.

Suggested Citation

  • Gallus, Jana & Bhatia, Sudeep, 2020. "Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 115-130.
    • Handle: RePEc:eee:jobhdp:v:160:y:2020:i:c:p:115-130
    DOI: 10.1016/j.obhdp.2020.02.003
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    2. Gallus, Jana & Reiff, Joseph & Kamenica, Emir & Fiske, Alan Page, 2021. "Relational Incentives Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, issue fothcomin.
    3. Gallus, Jana & Reiff, Joseph & Kamenica, Emir & Fiske, Alan Page, 2021. "Relational Incentives Theory," MPRA Paper 109898, University Library of Munich, Germany.
    4. Sudeep Bhatia & Lukasz Walasek & Paul Slovic & Howard Kunreuther, 2021. "The More Who Die, the Less We Care: Evidence from Natural Language Analysis of Online News Articles and Social Media Posts," Risk Analysis, John Wiley & Sons, vol. 41(1), pages 179-203, January.

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