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Studying cooperation and conflict between authors with history flow visualizations

Viégas, Fernanda B., Wattenberg, Martin, Dave, Kushal·2004·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

The Internet has fostered an unconventional and powerful style of collaboration: "wiki" web sites, where every visitor has the power to become an editor. In this paper we investigate the dynamics of Wikipedia, a prominent, thriving wiki. We make three contributions. First, we introduce a new exploratory data analysis tool, the history flow visualization, which is effective in revealing patterns within the wiki context and which we believe will be useful in other collaborative situations as well. Second, we discuss several collaboration patterns highlighted by this visualization tool and corroborate them with statistical analysis. Third, we discuss the implications of these patterns for the design and governance of online collaborative social spaces. We focus on the relevance of authorship, the value of community surveillance in ameliorating antisocial behavior, and how authors with competing perspectives negotiate their differences.

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Metadata

Authors
Viégas, F. B., Wattenberg, M., & Dave, K.
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2004-04-25
Pages
575-582
OpenAlex
W2111122424
Cited by
768
References
0
FWCI
76.45
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Studying cooperation and conflict between authors with history flow visualizations. https://app.cochat.ai/papers/10.1145/985692.985765
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Viégas, F. B., Wattenberg, M., & Dave, K. (2004). Studying cooperation and conflict between authors with history flow visualizations. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/985692.985765

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.