Toxic comments are associated with reduced activity of volunteer editors on Wikipedia - PubMed (original) (raw)

Toxic comments are associated with reduced activity of volunteer editors on Wikipedia

Ivan Smirnov et al. PNAS Nexus. 2023.

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.

© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.

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Figures

Fig. 1.

Fig. 1.

After receiving a toxic comment many users temporarily reduce their activity or leave the project completely. The figure shows the activity of 50 randomly selected users who received exactly one toxic comment. Blue squares indicate an active day, i.e. a day when at least one edit was done, starting from the first contribution of a given user. Red triangles correspond to toxic comments. Note that while some users are resilient and their activity is seemingly unaffected by toxic comments, many users temporarily reduce their activity or stop contributing altogether.

Fig. 2.

Fig. 2.

After receiving a toxic comment, users become less active. On average, users are more active near the time when they receive a toxic comment (peak at zero for the red line in panel a). Average activity across all users who have received a toxic comment is lower in all 100 days after the event compared to the corresponding days before (dashed and solid red lines in panel b). This cannot be explained by a baseline drop in activity after a nontoxic comment (dashed and solid blue lines in panel b). Similar results hold not only for the English edition but also for the other five editions (c–g).

Fig. 3.

Fig. 3.

The probability of leaving Wikipedia after receiving a toxic comment is substantially higher than might be expected otherwise. For all six editions the probability of leaving declines with the number of contributions. At the same time, this probability is substantially higher after receiving a toxic comment than might be expected otherwise. Dots are probability estimates and solid lines are the best linear fit on a log-log scale.

Fig. 4.

Fig. 4.

High levels of toxicity and targeted attacks could significantly reduce the number of active editors. Modeling results for a cohort of editors making their first contribution during the relatively stable phase of Wikipedia (shaded region in the inset). The model reproduces the general dynamics of user activity (blue line) but, as expected, cannot capture the COVID-19-related spike in activity. An extreme level of toxicity (red line) could reduce the cohort to virtually no active users, contrasting with a nontoxic environment (green line) or actual activity (blue line). Targeted attacks on newcomers (orange line) have the potential to significantly reduce the number of active contributors.

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