Harvard Business School’s Working Knowledge website has published an overview article today about a working paper written by Harvard Business School Professor Feng Zhu and Shane Greenstein from the Kellogg School of Business (Northwestern University).
The working paper is dated October 2014.
From Working Knowledge:
By identifying politically biased language in Encyclopedia Britannica and Wikipedia, Feng Zhu hopes to learn whether professional editors or open-sourced experts provide the most objective entries.
History, they say, is written by the victors, and can read very differently depending on who is telling the tale. Even modern-day issues such as immigration, gun control, abortion, and foreign policy are open to fervent debate depending on who is doing the opining. Over the years, Britannica has handled this uncertainty by seeking out the most distinguished experts in their fields in an attempt to provide a sober analysis on topics; while Wikipedia has urged its civilian editors to maintain what it calls a neutral point of view (NPOV).
But is objectivity better achieved by considering one viewpoint or thousands? Along with cowriter Shane Greenstein of Northwestern’s Kellogg School of Management, Zhu asks that question in a new paper, “Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopedia Britannica and Wikipedia“.
They found that in general, Wikipedia articles were more biased—with 73 percent of them containing code words, compared to just 34 percent in Britannica.
“We can only say [that] Wikipedia is more left. We can’t say which is reflecting true reality”In almost all cases, Wikipedia was more left-leaning than Britannica. Dividing articles into categories, the researchers found, for example, that stories on corporations were 11 percent more slanted toward Democrats, while observing similar leanings on topics such as government (9 percent), education (4 percent), immigration (4 percent), and civil rights (3 percent). Other categories did not have enough data to significantly identify bias.
Direct to Executive Summary and Author Abstract
Direct to Full Text Working Paper: Do Experts or Collective Intelligence Write with More Bias? Evidence from Encyclopedia Britannica and Wikipedia (36 pages; PDF).