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EVIDENCE from WIKIPEDIANS Shane Greenstein Yuan Gu NBER WORKING PAPER SERIES IDEOLOGICAL SEGREGATION AMONG ONLINE COLLABORATORS: EVIDENCE FROM WIKIPEDIANS Shane Greenstein Yuan Gu Feng Zhu Working Paper 22744 http://www.nber.org/papers/w22744 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 October 2016 We thank Erik Brynjolfsson, Marco Iansiti, Gerald Kane, Karim Lakhani, Abhishek Nagaraj, Frank Nagle, Michael Norton, Michael Toffel, Marshall Van Alstyne, Dennis Yao, and seminar participants at the INFORMS Annual Meeting 2015 and the Conference on Open and User Innovation 2016. We also thank Justin Ng and John Sheridan of HBS Research Computing Services for their research assistance, and Alicia Shems for her editorial assistance. We gratefully acknowledge financial support from the Division of Research of the Harvard Business School. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2016 by Shane Greenstein, Yuan Gu, and Feng Zhu. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Ideological Segregation among Online Collaborators: ¸˛Evidence from Wikipedians Shane Greenstein, Yuan Gu, and Feng Zhu NBER Working Paper No. 22744 October 2016 JEL No. L17,L3,L86 ABSTRACT Do online communities segregate into separate conversations when contributing to contestable knowledge involving controversial, subjective, and unverifiable topics? We analyze the contributors of biased and slanted content in Wikipedia articles about U.S. politics, and focus on two research questions: (1) Do contributors display tendencies to contribute to sites with similar or opposing biases and slants? (2) Do contributors learn from experience with extreme or neutral content, and does that experience change the slant and bias of their contributions over time? The findings show enormous heterogeneity in contributors and their contributions, and, importantly, an overall trend towards less segregated conversations. A higher percentage of contributors have a tendency to edit articles with the opposite slant than articles with similar slant. We also observe the slant of contributions becoming more neutral over time, not more extreme, and, remarkably, the largest such declines are found with contributors who interact with articles that have greater biases. We also find some significant differences between Republicans and Democrats. Shane Greenstein Feng Zhu Technology Operation and Management Morgan Hall 431 Morgan Hall 439 Harvard Business School Harvard Business School Boston, MA 02163 Soldiers Field [email protected] Boston, MA 02163 and NBER [email protected] Yuan Gu Harvard Business School Boston, MA. 02163 [email protected] Ideological Segregation among Online Collaborators: Evidence from Wikipedians Shane Greenstein, Yuan Gu, and Feng Zhu* [email protected], [email protected], and [email protected] Harvard Business School October 2016 Do online communities segregate into separate conversations when contributing to contestable knowledge involving controversial, subjective, and unverifiable topics? We analyze the contributors of biased and slanted content in Wikipedia articles about U.S. politics, and focus on two research questions: (1) Do contributors display tendencies to contribute to sites with similar or opposing biases and slants? (2) Do contributors learn from experience with extreme or neutral content, and does that experience change the slant and bias of their contributions over time? The findings show enormous heterogeneity in contributors and their contributions, and, importantly, an overall trend towards less segregated conversations. A higher percentage of contributors have a tendency to edit articles with the opposite slant than articles with similar slant. We also observe the slant of contributions becoming more neutral over time, not more extreme, and, remarkably, the largest such declines are found with contributors who interact with articles that have greater biases. We also find some significant differences between Republicans and Democrats. I. Introduction The growth of virtual communities that blur the boundaries between reader and writer has upended our understanding of processes for generating and consuming online content. These communities generate numerous cooperative and confrontational behaviors. The creation and consumption of content face challenging situations when online communities grapple with contested knowledge—which we define loosely for now as knowledge in which there is no single “right answer.” Such challenges arise most often when topics involve subjective, unverifiable, or * We thank Erik Brynjolfsson, Marco Iansiti, Gerald Kane, Karim Lakhani, Abhishek Nagaraj, Frank Nagle, Michael Norton, Michael Toffel, Marshall Van Alstyne, Dennis Yao, and seminar participants at the INFORMS Annual Meeting 2015 and the Conference on Open and User Innovation 2016. We also thank Justin Ng and John Sheridan of HBS Research Computing Services for their research assistance, and Alicia Shems for her editorial assistance. We gratefully acknowledge financial support from the Division of Research of the Harvard Business School. controversial information—as is common in politically oriented ideological debates, for example. Contested knowledge presents challenges because online communities bring together participants who originate from different traditions, have different ways of expressing opinions, have different cultural and historical foundations for those opinions, and potentially have different bases of facts (e.g., Arazy et al. 2011). While many studies have examined the processes by which communities resolve conflicts among distinct perspectives, there is lack of quantitative research about the processes leading to outcomes, especially in the most challenging situations, such as with debates involving contested knowledge. Segregated and unsegregated conversations are polar opposites, and they have captured speculation about the outcome of debates involving contested knowledge. In an unsegregated conversation, the community engages people with diverse ideas and facilitates a conversation between participants with opposing views (Benkler 2006) until participants reach a consensus. In a segregated conversation, like-minded participants self-select into supplying content for others with similar views and read only the content from those with whom they already agree. This behavior polarizes information consumption and sharing (e.g., Mullainathan and Shleifer 2005, Sunstein 2001), creating segregated “small villages” (e.g., Gentzkow and Shapiro 2003, Van Alstyne and Brynjolfsson 2005). Our study brings empirical measurement to this topic and analyzes the micro-behavior that supports or undermines segregated conversations. This study develops a measurement approach for characterizing the tendency of types of contributors to offer slanted contributions to content that may already contain slanted content. As with Greenstein and Zhu (2012, 2016), we adapt the method developed by Gentzkow and Shapiro (2010) for rating newspaper editorials to rate the bias and slant of Wikipedia’s content, i.e., its articles. In these ratings, slant denotes degree of opinion along a continuous yardstick, from extreme degrees of red (e.g., Republican) to extreme degrees of blue (e.g., Democrat), and all the shades of purple in between. Bias is the absolute value of this yardstick from its zero point, and thus denotes the strength of the opinion. New to this research, we develop a method for characterizing the slant of Wikipedia’s contributors, i.e., an editor’s tendency to make editorial changes that move articles towards more red or blue slant. We analyze two key aspects of contributor micro-behavior, namely, (1) the slant of a target selected for a contribution, and (2) the evolution of a contributor’s slant over time. Specifically, we first ask: Do contributors display tendencies to edit sites with similar or opposing biases and 1 slants? If contributors tend to edit articles that agree with their own slant, we label this event Birds of a Feather, or BOF. In contrast, if contributors tend to suggest contributions to content that disagrees with their own slant, we call such a process Opposites Attract, or OA. We then ask whether and how experiencing BOF and OA changes the slant and bias of a contributor over time. We ask: Do contributors learn from extreme or neutral content, and does that experience change the slant and bias of their contribution? Together these two tendencies characterize the propensity to have (un)segregated conversations. The setting for this investigation is the editorial histories of articles about U.S. politics published in Wikipedia on January 16, 2011. Wikipedia offers a rich setting for investigating micro-behavior behind segregated conversations because all revisions are well documented. We examine the latest version of 70,305 articles about U.S. political topics, which receive contributions from 2,891,877 unique contributors. As with prior work (e.g., Greenstein and Zhu, 2012, 2016), we characterize all articles for bias and slant. More novel, in this study we develop a rating of the bias and slant of the contributors by measuring
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