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Characterizing Wikipedia pages using edit network motif profiles

Published: 28 October 2011 Publication History

Abstract

Good Wikipedia articles are authoritative sources due to the collaboration of a number of knowledgeable contributors. This is the many eyes idea. The edit network associated with a Wikipedia article can tell us something about its quality or authoritativeness. In this paper we explore the hypothesis that the characteristics of this edit network are predictive of the quality of the corresponding article's content. We characterize the edit network using a profile of network motifs and we show that this network motif profile is predictive of the Wikipedia quality classes assigned to articles by Wikipedia editors. We further show that the network motif profile can identify outlier articles particularly in the 'Featured Article' class, the highest Wikipedia quality class.

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  • (2023)Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature ReviewACM Computing Surveys10.1145/362528656:4(1-37)Online publication date: 10-Nov-2023
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    cover image ACM Conferences
    SMUC '11: Proceedings of the 3rd international workshop on Search and mining user-generated contents
    October 2011
    100 pages
    ISBN:9781450309493
    DOI:10.1145/2065023
    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: 28 October 2011

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    Author Tags

    1. authoritativeness
    2. network motifs
    3. wikipedia

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    View all
    • (2023)Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature ReviewACM Computing Surveys10.1145/362528656:4(1-37)Online publication date: 10-Nov-2023
    • (2022)sGrow: Explaining the Scale-Invariant Strength Assortativity of Streaming ButterfliesACM Transactions on the Web10.1145/357240817:3(1-46)Online publication date: 14-Dec-2022
    • (2022)Understanding the characteristics of COVID-19 misinformation communities through graphlet analysisOnline Social Networks and Media10.1016/j.osnem.2021.10017827(100178)Online publication date: Jan-2022
    • (2021)Recognizing Influential Nodes in Social Networks With Controllability and ObservabilityIEEE Internet of Things Journal10.1109/JIOT.2020.30404878:8(6197-6204)Online publication date: 15-Apr-2021
    • (2021)Measuring Quality of Wikipedia Articles by Feature Fusion‐based Stack LearningProceedings of the Association for Information Science and Technology10.1002/pra2.44958:1(206-217)Online publication date: 13-Oct-2021
    • (2019)Understanding the Signature of Controversial Wikipedia Articles through Motifs in Editor Revision NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316754(1180-1187)Online publication date: 13-May-2019
    • (2019)A Survey of Measures for Network MotifsIEEE Access10.1109/ACCESS.2019.29267527(106576-106587)Online publication date: 2019
    • (2019)Team diversity, polarization, and productivity in online peer productionSocial Network Analysis and Mining10.1007/s13278-019-0569-79:1Online publication date: 22-Jun-2019
    • (2018)Diverse teams tend to do good work in Wikipedia (but jacks of all trades don't)Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3382225.3382271(214-221)Online publication date: 28-Aug-2018
    • (2018)Knowledge categorization affects popularity and quality of Wikipedia articlesPLOS ONE10.1371/journal.pone.019067413:1(e0190674)Online publication date: 2-Jan-2018
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