Do experts or collective intelligence write with more bias?
Subtitle: evidence from Encyclopædia Britannica and Wikipedia
By: Shane Greenstein, Feng Zhu
Contributors: Zhu, Feng, author; Harvard Business School
Series: Working paper / Harvard Business School -- 15-023; Working paper (Harvard Business School) -- 15-023
Publisher: Harvard Business School
Published: 2014
Place of publication: Boston]
Language: eng
Number of pages: 34
Description: Which source of information contains greater bias and slant -- text written by an expert or that constructed via collective intelligence? Do the costs of acquiring, storing, displaying and revising information shape those differences? We evaluate these questions empirically by examining slanted and biased phrases in content on US political issues from two sources -- Encyclopædia Britannica and Wikipedia. Our overall slant measure is less (more) than zero when an article leans towards Democrat (Republican) viewpoints, while bias is the absolute value of the slant. Using a matched sample of pairs of articles from Britannica and Wikipedia, we show that, overall, Wikipedia articles are more slanted towards Democrat than Britannica articles, as well as more biased. Slanted Wikipedia articles tend to become less biased than Britannica articles on the same topic as they become substantially revised, and the bias on a per word basis hardly differs between the sources. These results have implications for the segregation of readers in online sources and the allocation of editorial resources in online sources using collective intelligence.
Notes: "October 2014" -- Publisher's website.

Includes bibliographical references (pages 17-20).