Una cosa divertente che si dice di Wikipedia è che funziona in pratica ma non in teoria. E scoprire perchè funziona, scientificamente (do per assunto che sì, funziona, quindi se non siete d’accordo smettete pure qui) non è affatto semplice.
Di ricerche su Wikipedia se ne fanno tante, e l’oggetto di studio è oltremodo complesso (cosa guardiamo? il formarsi di una comunità? come interagisce? la distribuzione sessuale? l’obiettività degli articoli? come la misuriamo? ecc.). Il gruppo di ricerca della Wikimedia Foundation è ovviamente uno dei più attivi in materia, ma fa troppe cose per seguirle tutte (qui c’è l’archivio, se volete spulciare).
La questione della neutralità degli articoli, però, fa parte delle domande più importanti, perchè va al nocciolo della questione, che è capire perchè Wikipedia funziona, e come e quando scatta quella del crowdsourcing che chiamiamo “saggezza delle massa” (wisdom of the crowds)(PS: a proposito, leggete Reinventing Discovery, che è bellissimo e parla di tutto questo, però applicato alla scienza (e lì si chiama citizen science)).
Copioincollo un paragrafo dell’ultima newsletter, il resto lo trovate qui. Poi, se mi viene voglia e mi aiutate, magari recuperiamo anche ricerche pregresse sullo stesso argomento.
Given enough eyeballs, do articles become neutral?
Building on their previously reviewed research, Greenstein and Zhu ask[14] “will enough eyeballs eliminate or decrease the amount of bias when information is controversial, subjective, and unverifiable?” Their research calls this into question, by taking a statistical approach to measuring bias in Wikipedia articles about US political topics, which uses Linus’ Law (“Given enough eyeballs, all bugs are shallow”) as a null hypothesis.
They rely on a slant index previously developed for studying news media bias, which specifies certain code words as indicating Republican or Democratic bias. Within their sample of 28,382 articles relating to American politics, they find that the category and vintage of an article are most predictive of bias. “Topics of articles with the most Democrat words are civil rights, gun control, and homeland security. Those with the most Republican words are abortion, foreign policy, trade, tax reform, and taxation. … [T]he slant and bias are most pronounced for articles born in 2002 and 2003″. While they do not find a neutral point of view within each article or topic, across articles, Wikipedia balances Democratic and Republican points of view.
Yet answering “Why did Wikipedia become less biased over time?” is more challenging. They classify explanatory variables into three groups: attention and editing; dispersion of contributions; and article features. The narrow interpretation of Linus’ Law would make attention and editing the only relevant feature (not supported by their data), while a broader interpretation would also take dispersion into account (weak support from their data). While both the number of revisions and the number of editor usernames are statistically significant, they work in opposite directions. Pageviews, while also statistically significant, are unavailable before February 2007. They also suggest questions for further work, including improvements to their revision sampling (they “divide [each article’s] revisions into ten revisions of equal length”) and overall sampling method (which uses the same techniques as their earlier work).
aubreymcfato.com 