Do experts or collective intelligence write with more bias?
evidence from Encyclopædia Britannica and Wikipedia
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Author
Contributions
- Zhu, Feng, author - Contributor
- Harvard Business School - Contributor
Publication
2014 - Harvard Business School, Boston], Massachusetts
Language
English
Word Count
0 words, Guess
Page Count
0 pages
Identifiers
- OCLC Control Number893976163
- Open LibraryOL53040487M
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."
Subjects
Series Statement
- Working paper / Harvard Business School -- 15-023
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