One of my more social-science-y interests lately has been in reverse-engineering the rationale for nation-state censorship policies from the data available. Any published rationale is almost always quite vague (
harmful to public order,
blasphemous, sort of thing). Hard data in this area consists of big lists of URLs, domain names, and/or keywords that are known or alleged to be blocked. Keywords are great, when you can get them, but URLs are less helpful, and domain names even less so. I have a pretty good idea why the Federal Republic of Germany might have a problem with a site that sells sheet music of traditional European folk songs (actual example from #BPjMleak), but I don’t know which songs are at stake, because they blocked the entire site. I could find out, but I’d probably want a dose of brain bleach afterward. More to the point, no matter how strong my stomach is, I don’t have the time for the amount of manual research required to figure out the actual issue with all 3000 of the sites on that list—and that’s just one country, whose politics and history are relatively well-known to me.
So, today’s paper is about mechanically identifying controversial Wikipedia articles. Specifically, they look through the revision history of each article for what they call mutual reverts, where two editors have each rolled back the other’s work. This is a conservative measure; edit warring on Wikipedia can be much more subtle. However, it’s easy to pick out mechanically. Controversial articles are defined as those where there are many mutual reverts, with extra weight given to mutual reverts by pairs of
senior editors (people with many contributions to the entire encyclopedia). They ran this analysis for ten different language editions, and the bulk of the article is devoted to discussing how each language has interesting peculiarities in what is controversial. Overall, there’s strong correlation across languages, strong correlation with external measures of political or social controversy, and strong correlation with the geographic locations where each language is spoken. An interesting exception to that last is that the Middle East is controversial in all languages, even those that are mostly spoken very far from there; this probably reflects the ongoing wars in that area, which have affected everyone’s politics at least somewhat.
What does this have to do with nation-state censorship? Well, politically controversial topics in language X ought to be correlated with topics censored by nation-states where X is commonly spoken. There won’t be perfect alignment; there will be topics that get censored that nobody bothers to argue about on Wikipedia (pornography, for instance) and there will be topics of deep and abiding Wikipedia controversy that nobody bothers to censor (Spanish football teams, for instance). But if an outbound link from a controversial Wikipedia article gets censored, it is reasonably likely that the censorship rationale has something to do with the topic of the article. The same is true of censored pages that share a significant number of keywords with a controversial article. It should be good enough for hypothesis generation and rough classification of censored pages, at least.