DO EXPERTS OR CROWD-BASED MODELS PRODUCE MORE BIAS? EVIDENCE FROM ENCYCLOPEDIA BRITANNICA AND WIKIPEDIA

• 成果类型: Article
• 署名作者: Greenstein, Shane; Zhu, Feng
• 署名单位: Harvard University
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2018/14084
• 发表日期: 2018
• 页码: 945-+
• 关键词: virtual communities; INFORMATION; internet; KNOWLEDGE; mechanisms; valuation; QUALITY; wikis

摘要

Organizations today can use both crowds and experts to produce knowledge. While prior work compares the accuracy of crowd-produced and expert-produced knowledge, we compare bias in these two models in the context of contested knowledge, which involves subjective, unverifiable, or controversial information. Using data from Encyclopedia Britannica, authored by experts, and Wikipedia, an encyclopedia produced by an online community, we compare the slant and bias of pairs of articles on identical topics of U.S. politics. Our slant measure is less (more) than zero when an article leans toward Democratic (Republican) viewpoints, while bias is the absolute value of the slant. We find that Wikipedia articles are more slanted toward Democratic views than are Britannica articles, as well as more biased. The difference in bias between a pair of articles decreases with more revisions. The bias on a per word basis hardly differs between the sources because Wikipedia articles tend to be longer than Britannica articles. These results highlight the pros and cons of each knowledge production model, help identify the scope of the empirical generalization of prior studies comparing the information quality of the two production models, and offer implications for organizations managing crowdbased knowledge production.