Collective Intelligence and Neutral Point of View: The Case of Wikipedia* Shane Greenstein Kellogg School of Management Northwestern University Evanston, IL 60208 & Harvard Business School Boston, MA 02163
[email protected] Feng Zhu Harvard Business School Boston, MA 02163
[email protected] July 14, 2013 _______________________ * We thank Megan Busse, Michelle Deveraux, Karim Lakhani, Gil Penchina, Scott Stern, Monic Sun, Joel Waldfogel and seminar participants at the Allied Social Sciences, at Indiana University, MIT, Northwestern, University of Chicago, University of Colorado, University of Florida, and the University of Toronto for useful conversations. We thank the Wikimedia Foundation for providing access to data. We are responsible for all remaining errors. Collective Intelligence and Neutral Point of View: The Case of Wikipedia Abstract An increasing number of organizations or communities today are harnessing the power of collective intelligence to tackle problems that are too big to be solved by themselves. While several prior studies have shown that collective intelligence can lead to high- quality output in the context of uncontroversial and verifiable information, it is unclear whether a production model based on collective intelligence will produce any desirable outcome when information is controversial, subjective, and unverifiable. We examine whether collective intelligence helps achieve a neutral point of view (NPOV) using data from Wikipedia’s articles on US politics. Our null hypothesis builds on Linus’ Law, often expressed as “Given enough eyeballs, all bugs are shallow.” Our findings are consistent with a narrow interpretation of Linus’ Law, namely, a greater number of contributors to an article makes an article more neutral. No evidence supports a broad interpretation of Linus’ Law.