The Wisdom of Polarized Crowds
Not on the map. Why?
Abstract (arXiv)
As political polarization in the United States continues to rise, the question of whether polarized individuals can fruitfully cooperate becomes pressing. Although diversity of individual perspectives typically leads to superior team performance on complex tasks, strong political perspectives have been associated with conflict, misinformation and a reluctance to engage with people and perspectives beyond one's echo chamber. It is unclear whether self-selected teams of politically diverse individuals will create higher or lower quality outcomes. In this paper, we explore the effect of team political composition on performance through analysis of millions of edits to Wikipedia's Political, Social Issues, and Science articles. We measure editors' political alignments by their contributions to conservative versus liberal articles. A survey of editors validates that those who primarily edit liberal articles identify more strongly with the Democratic party and those who edit conservative ones with the Republican party. Our analysis then reveals that polarized teams---those consisting of a balanced set of politically diverse editors---create articles of higher quality than politically homogeneous teams. The effect appears most strongly in Wikipedia's Political articles, but is also observed in Social Issues and even Science articles. Analysis of article "talk pages" reveals that politically polarized teams engage in longer, more constructive, competitive, and substantively focused but linguistically diverse debates than political moderates. More intense use of Wikipedia policies by politically diverse teams suggests institutional design principles to help unleash the power of politically polarized teams.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Nov 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 2; 2025: 2.
9 papers.
- 2024Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulationcited by 13
- 2021Reason Against the Machine: Future Directions for Mass Online Deliberationcited by 12
- 2024AI and the Future of Digital Public Squarescited by 6
- 2021Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regressioncited by 1
- 2022Considerations for Multilingual Wikipedia Researchcited by 1
- 2023Exploitation and exploration in text evolution. Quantifying planning and translation flows during writingcited by 1
- 2025Characterizing Knowledge Manipulation in a Russian Wikipedia Forkcited by 1
- 2021Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoderscited by 0
- 2025Measuring and Analyzing Subjective Uncertainty in Scientific Communicationscited by 0
- 2025Characterizing Knowledge Manipulation in a Russian Wikipedia Fork14 Apr 2025
- 2025Measuring and Analyzing Subjective Uncertainty in Scientific Communications27 Mar 2025
- 2024AI and the Future of Digital Public Squares13 Dec 2024
- 2024Evolving AI Collectives to Enhance Human Diversity and Enable Self-Regulation19 Feb 2024
- 2023Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing7 Feb 2023
- 2022Considerations for Multilingual Wikipedia Research5 Apr 2022
- 2021Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression15 Aug 2021
- 2021Reason Against the Machine: Future Directions for Mass Online Deliberation27 Jul 2021
- 2021Semantic maps and metrics for science Semantic maps and metrics for science using deep transformer encoders13 Apr 2021