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.
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.