Article

A content-driven reputation system for the wikipedia

Authors:

B. Thomas Adler,

Luca de AlfaroAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 261 - 270

https://doi.org/10.1145/1242572.1242608

Published: 08 May 2007 Publication History

Abstract

We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691, 551 pages and 5, 587, 523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms.

References

[1]

R. Burns and D. Long. A linear time, constant space differencing algorithm. In Performance, Computing, and Communication Conference (IPCCC), pages 429--436. IEEE International, 1997.

[2]

T. Cover and J. Thomas. Elements of Information Theory. J. Wiley & Sons, 1991.

Digital Library

[3]

T. Cross. Puppy smoothies: Improving the reliability of open, collaborative wikis. First Monday, 11(9), September 2006.

[4]

W. Cunningham and B. Leuf. The Wiki Way. Quick Collaboration on the Web. Addison-Wesley, 2001.

Digital Library

[5]

C. Dellarocas. The digitization of word-of-mouth: Promises and challenges of onlin reputation systems. Management Science, October 2003.

Digital Library

[6]

J. Golbeck. Computing and Applying Trust in Web-Based Social Networks. PhD thesis, University of Maryland, 2005.

Digital Library

[7]

R. Guha, R. Kumar, P. Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. of the 13th Intl. Conf. on World Wide Web, pages 403--412. ACM Press, 2004.

Digital Library

[8]

J. Hunt and M. McIlroy. An algorithm for differential file comparison. Computer Science Technical Report 41, Bell Laboratories, 1975.

[9]

S. Kamvar, M. Schlosser, and H. Garcia-Molina. The eigentrust algorithm for reputation management in p2p networks. In Proc. of the 12th Intl. Conf. on World Wide Web, pages 640--651. ACM Press, 2003.

Digital Library

[10]

J. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604--632, 1999.

Digital Library

[11]

V. Livshits and T. Zimmerman. Dynamine: Finding common error patterns by mining software revision histories. In ESEC/FSE, pages 296--305, 2005.

Digital Library

[12]

D. Lucking-Reiley, D. Bryan, N. Prasad, and D. Reeves. Pennies from Ebay: The determinants of price in online auctions. Working paper, Vanderbilt University, 1999.

[13]

D. McGuinness, H. Zeng, Pda Silva, LDing, DNarayanan, and MBhaowal. Investigation into trust for collaborative information repositories: A Wikipedia case study. In Proceedings of the Workshop on Models of Trust for the Web, 2006.

[14]

E. Myers. An o(ND) difference algorithm and its variations. Algorithmica, 1(2):251--266, 1986.

[15]

L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank citation ranking: Bringing order to the web. Technical report, Stanford Digital Library Technologies Project, 1998.

[16]

P. Resnick, R. Zeckhauser, E. Friedman, and K. Kiwabara. Reputation systems. Comm. ACM, 43(12):45--48, 2000.

Digital Library

[17]

W. Tichy. The string-to-string correction problem with block move. ACM Transactions on Computer Systems, 2(4), 1984.

Digital Library

[18]

F. Viégas, M. Wattenberg, and K. Dave. Studying cooperation and conflict between authors with history flow visualizations. In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pages 575--582, 2004.

Digital Library

[19]

H. Zeng, M. Alhoussaini, L. Ding, R. Fikes, and D. McGuinness. Computing trust from revision history. In Intl. Conf. on Privacy, Security and Trust, 2006.

Digital Library

Cited By

Lambert CChoi FChandrasekharan E(2024)"Positive reinforcement helps breed positive behavior": Moderator Perspectives on Encouraging Desirable BehaviorProceedings of the ACM on Human-Computer Interaction10.1145/36869298:CSCW2(1-33)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686929
Laniado DMauri MBorra E(2024)Chapter 6. Exploring the evolution of Wikipedia articles through ContropediaInvestigating Wikipedia10.1075/scl.121.06lan(156-177)Online publication date: 25-Oct-2024
https://doi.org/10.1075/scl.121.06lan
Bai ASatarpour MMohebbi FForati A(2024)Digital Crowdsourcing and VGI: impact on information quality and business intelligenceSpatial Information Research10.1007/s41324-024-00572-232:4(463-471)Online publication date: 1-Feb-2024
https://doi.org/10.1007/s41324-024-00572-2
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Index Terms

A content-driven reputation system for the wikipedia
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Computer supported cooperative work
  2. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Computer supported cooperative work

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Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 May 2007

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WWW'07

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ACM

WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Lambert CChoi FChandrasekharan E(2024)"Positive reinforcement helps breed positive behavior": Moderator Perspectives on Encouraging Desirable BehaviorProceedings of the ACM on Human-Computer Interaction10.1145/36869298:CSCW2(1-33)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686929
Laniado DMauri MBorra E(2024)Chapter 6. Exploring the evolution of Wikipedia articles through ContropediaInvestigating Wikipedia10.1075/scl.121.06lan(156-177)Online publication date: 25-Oct-2024
https://doi.org/10.1075/scl.121.06lan
Bai ASatarpour MMohebbi FForati A(2024)Digital Crowdsourcing and VGI: impact on information quality and business intelligenceSpatial Information Research10.1007/s41324-024-00572-232:4(463-471)Online publication date: 1-Feb-2024
https://doi.org/10.1007/s41324-024-00572-2
Vargo ATag BBlakely CKise K(2023)Poverty Traps in Online Knowledge-Based Peer-Production CommunitiesInformatics10.3390/informatics1003006110:3(61)Online publication date: 13-Jul-2023
https://doi.org/10.3390/informatics10030061
Martel CAllen JPennycook GRand D(2023)Crowds Can Effectively Identify Misinformation at ScalePerspectives on Psychological Science10.1177/1745691623119038819:2(477-488)Online publication date: 18-Aug-2023
https://doi.org/10.1177/17456916231190388
Yu YMcDonald D(2023)"Why do you need 400 photographs of 400 different Lockheed Constellation?": Value Expressions by Contributors and Users of Wikimedia CommonsProceedings of the ACM on Human-Computer Interaction10.1145/36100947:CSCW2(1-34)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610094
García-Méndez SLeal FMalheiro BBurguillo-Rial J(2023)Interpretable Classification of Wiki-Review StreamsIEEE Access10.1109/ACCESS.2023.334247211(141137-141151)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3342472
Rizzo LLongo L(2023)Comparing and extending the use of defeasible argumentation with quantitative data in real-world contextsInformation Fusion10.1016/j.inffus.2022.08.02589(537-566)Online publication date: Jan-2023
https://doi.org/10.1016/j.inffus.2022.08.025
Bidgoly AArabi F(2023)Robustness evaluation of trust and reputation systems using a deep reinforcement learning approachComputers and Operations Research10.1016/j.cor.2023.106250156:COnline publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1016/j.cor.2023.106250
Zhao YWei XLiu YLiao Z(2022)A Reputation Model of OSM Contributor Based on Semantic Similarity of Ontology ConceptsApplied Sciences10.3390/app12221136312:22(11363)Online publication date: 9-Nov-2022
https://doi.org/10.3390/app122211363
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