Category: wikipedia

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A new paper on the risk of nationalist governance capture in self-governed Wikipedia projects

Wikipedia is one of the most visited websites in the world and the largest online repository of human knowledge. It is also both a target of and a defense against misinformation, disinformation, and other forms of online information manipulation. Importantly, its 300 language editions are self-governed—i.e., they set most of their rules and policies. Our new paper asks: What types of governance arrangements make some self-governed online groups more vulnerable to disinformation campaigns? We answer this question by comparing two Wikipedia language editions—Croatian and Serbian Wikipedia. Despite relying on common software and being situated in a common sociolinguistic environment, these communities differed in how successfully they responded to disinformation-related threats.

For nearly a decade, the Croatian language version of Wikipedia was run by a cabal of far-right nationalists who edited articles in ways that promoted fringe political ideas and involved cases of historical revisionism related to the Ustaše regime, a fascist movement that ruled the Nazi puppet state called the Independent State of Croatia during World War II. This cabal seized complete control of the governance of the encyclopedia, banned and blocked those who disagreed with them, and operated a network of fake accounts to give the appearance of grassroots support for their policies.

Thankfully, Croatian Wikipedia appears to be an outlier. Though both the Croatian and Serbian language editions have been documented to contain nationalist bias and historical revisionism, Croatian Wikipedia alone seems to have succumbed to governance capture: a takeover of the project’s mechanisms and institutions of governance by a small group of users.

The situation in Croatian Wikipedia was well-documented and is now largely fixed, but still know very little about why Croatian Wikipedia was taken over, while other language editions seem to have rebuffed similar capture attempts. In a new paper that is accepted for publication in the Proceedings of the ACM: Human-Computer Interaction (CSCW), we present an interview-based study that tries to explain why Croatian was captured while several other editions facing similar contexts and threats fared better.

Short video presentation of the work given at Wikimania in August 2023.

We interviewed 15 participants from both the Croatian and Serbian Wikipedia projects, as well as the broader Wikimedia movement. Based on insights from these interviews, we arrived at three propositions that, together, help explain why Croatian Wikipedia succumbed to capture while Serbian Wikipedia did not: 

  1. Perceived Value as a Target. Is the project worth expending the effort to capture?
  2. Bureaucratic Openness. How easy is it for contributors outside the core founding team to ascend to local governance positions?
  3. Institutional Formalization. To what degree does the project prefer personalistic, informal forms of organization over formal ones?
The conceptual model from our paper, visualizing possible institutional configurations among Wikipedia projects that affect the risk of governance capture. 

We found that both Croatian Wikipedia and Serbian Wikipedia were attractive targets for far-right nationalist capture due to their sizable readership and resonance with a national identity. However, we also found that the two projects diverged early on in their trajectories in terms of how open they remained to new contributors ascending to local governance positions and the degree to which they privileged informal relationships over formal rules and processes as organizing principles of the project. Ultimately, Croatian’s relative lack of bureaucratic openness and rules constraining administrator behavior created a window of opportunity for a motivated contingent of editors to seize control of the governance mechanisms of the project. 

Though our empirical setting was Wikipedia, our theoretical model may offer insight into the challenges faced by self-governed online communities more broadly. As interest in decentralized alternatives to Facebook and X (formerly Twitter) grows, communities on these sites will likely face similar threats from motivated actors. Understanding the vulnerabilities inherent in these self-governing systems is crucial to building resilient defenses against threats like disinformation. 

For more details on our findings, take a look at the preprint of our paper.

Preprint on arxiv.org: https://arxiv.org/abs/2311.03616. The paper has been accepted for publication in Proceedings of the ACM on Human-Computer Interaction (CSCW) and will be presented at CSCW in 2024. This blog post and the paper it describes are collaborative work by Zarine Kharazian, Benjamin Mako Hill, and Kate Starbird.

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Let’s talk about taboo! A new paper on how taboo shapes activity on Wikipedia

Taboo subjects—such as sexuality and mental health—are as important to discuss as they are difficult to raise in conversation. Although many people turn to online resources for information on taboo subjects, censorship and low quality information are common in search results. In work that has just been published at CSCW this week, we present a series of analyses that describe how taboo shapes the process of collaborative knowledge building on English Wikipedia. Our work shows that articles on taboo subjects are much more popular and the subject of more vandalism than articles on non-taboo topics. In surprising news, we also found that they were edited more often and were of higher quality! We also found that contributors to taboo articles did less to hide their identity than we expected.

Short video of a our presentation of the work given at Wikimania in August 2023.

The first challenge we faced in conducting our study was building a list of Wikipedia articles on taboo topics. This was challenging because while taboo is deeply cultural and can seem natural, our individual perspectives of what is and isn’t taboo is privileged and limited. In building our list, we wanted to avoid relying on our own intuition about what qualifies as taboo. Our approach was to make use of an insight from linguistics: people develop euphemisms as ways to talk about taboos. Think about all the euphemisms we’ve devised for death, or sex, or menstruation, or mental health. Using figurative languages lets us distance ourselves from the pollution of a taboo.

We used this insight to build a new machine learning classifier based on dictionary definitions in English Wiktionary. If a ‘sense’ of a word was tagged as a euphemism, we treated the words in the definition as indicators of taboo. The end result of this analysis is a series of words and phrases that most powerfully differentiate taboo from non-taboo. We then did a simple match between those words and phrases and Wikipedia article titles. We built a comparison sample of articles whose titles are words that, like our taboo articles, appear in Wiktionary definitions.

We used this new dataset to test a series of hypotheses about how taboo shapes collaborative production in Wikipedia. Our initial hypotheses were based on the idea that taboo information is often in high demand but that Wikipedians might be reluctant to associate their names (or usernames) with taboo topics. The result, we argued, would be articles that were in high demand but of low quality. What we found was that taboo articles are thriving on Wikipedia! In summary, we found in comparison to non-taboo articles:

  • Taboo articles are more popular (as expected).
  • Taboo articles receive more contributions (contrary to expectations).
  • Taboo articles receive more low-quality contributions (as expected).
  • Taboo articles are higher quality (contrary to expectations).
  • Taboo article contributors are more likely to contribute without an account (as expected), and have less experience (as expected), but that accountholders are more likely to make themselves more identifiable by having a user page, disclosing their gender, and making themselves emailable (all three of these are contrary to expectation!).

For more details, visualizations, statistics, and more, we hope you’ll take a look at our paper. If you are attending CSCW in October 2023, we also hope and come to our CSCW presentation in Minneapolis!

The full citation for the paper is: Champion, Kaylea, and Benjamin Mako Hill. 2023. “Taboo and Collaborative Knowledge Production: Evidence from Wikipedia.” Proceedings of the ACM on Human-Computer Interaction 7 (CSCW2): 299:1-299:25. https://doi.org/10.1145/3610090.

We have also released replication materials for the paper, including all the data and code used to conduct the analyses.

This blog post and the paper it describes are collaborative work by Kaylea Champion and Benjamin Mako Hill.