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Toxic comments are associated with reduced activity of volunteer editors on Wikipedia
by
Oprea, Camelia
, Smirnov, Ivan
, Strohmaier, Markus
in
Agent-based models
/ Collaboration
/ Editors
/ Encyclopedias
/ Online social networks
/ Social and Political Sciences
/ Social aspects
/ Social networks
/ Speech
/ Technology application
/ Toxicity
/ Volunteers
2023
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Toxic comments are associated with reduced activity of volunteer editors on Wikipedia
by
Oprea, Camelia
, Smirnov, Ivan
, Strohmaier, Markus
in
Agent-based models
/ Collaboration
/ Editors
/ Encyclopedias
/ Online social networks
/ Social and Political Sciences
/ Social aspects
/ Social networks
/ Speech
/ Technology application
/ Toxicity
/ Volunteers
2023
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Do you wish to request the book?
Toxic comments are associated with reduced activity of volunteer editors on Wikipedia
by
Oprea, Camelia
, Smirnov, Ivan
, Strohmaier, Markus
in
Agent-based models
/ Collaboration
/ Editors
/ Encyclopedias
/ Online social networks
/ Social and Political Sciences
/ Social aspects
/ Social networks
/ Speech
/ Technology application
/ Toxicity
/ Volunteers
2023
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Toxic comments are associated with reduced activity of volunteer editors on Wikipedia
Journal Article
Toxic comments are associated with reduced activity of volunteer editors on Wikipedia
2023
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Overview
Abstract
Wikipedia is one of the most successful collaborative projects in history. It is the largest encyclopedia ever created, with millions of users worldwide relying on it as the first source of information as well as for fact-checking and in-depth research. As Wikipedia relies solely on the efforts of its volunteer editors, its success might be particularly affected by toxic speech. In this paper, we analyze all 57 million comments made on user talk pages of 8.5 million editors across the six most active language editions of Wikipedia to study the potential impact of toxicity on editors’ behavior. We find that toxic comments are consistently associated with reduced activity of editors, equivalent to 0.5–2 active days per user in the short term. This translates to multiple human-years of lost productivity, considering the number of active contributors to Wikipedia. The effects of toxic comments are potentially even greater in the long term, as they are associated with a significantly increased risk of editors leaving the project altogether. Using an agent-based model, we demonstrate that toxicity attacks on Wikipedia have the potential to impede the progress of the entire project. Our results underscore the importance of mitigating toxic speech on collaborative platforms such as Wikipedia to ensure their continued success.
Publisher
Oxford University Press
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