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Measuring the quality of web content using factual information

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Michael GranitzerAuthors Info & Claims

WebQuality '12: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality

Pages 7 - 10

https://doi.org/10.1145/2184305.2184308

Published: 16 April 2012 Publication History

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Abstract

Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.

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Cited By

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Moás PLopes C(2023)Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature ReviewACM Computing Surveys10.1145/362528656:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3625286
Chhabra ASrivastava SS. Iyengar SSaini P(2021)Structural Analysis of Wikigraph to Investigate Quality Grades of Wikipedia ArticlesCompanion Proceedings of the Web Conference 202110.1145/3442442.3452345(584-590)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452345
Hou JLi JWang P(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
https://doi.org/10.1002/pra2.449
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Index Terms

Measuring the quality of web content using factual information
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

WebQuality '12: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality

April 2012

71 pages

ISBN:9781450312370

DOI:10.1145/2184305

Conference Chairs:
Carlos Castillo
Yahoo! Research
,
Zoltan Gyongyi
Google Research
,
Adam Jatowt
Kyoto University
,
Katsumi Tanaka
Kyoto University

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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Association for Computing Machinery

New York, NY, United States

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Published: 16 April 2012

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WebQuality '12

WebQuality '12: Joint WICOW/AIRWeb Workshop on Web Quality

April 16, 2012

Lyon, France

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Moás PLopes C(2023)Automatic Quality Assessment of Wikipedia Articles—A Systematic Literature ReviewACM Computing Surveys10.1145/362528656:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3625286
Chhabra ASrivastava SS. Iyengar SSaini P(2021)Structural Analysis of Wikigraph to Investigate Quality Grades of Wikipedia ArticlesCompanion Proceedings of the Web Conference 202110.1145/3442442.3452345(584-590)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442442.3452345
Hou JLi JWang P(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
https://doi.org/10.1002/pra2.449
Chang JCheng JDanescu-Niculescu-Mizil C(2020)Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online DiscussionsProceedings of The Web Conference 202010.1145/3366423.3380273(2066-2077)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380273
Bazán Pereyra GCuello CCapodici GJofré VFerretti EBonnin RErrecalde M(2020)Predicting Information Quality Flaws in Wikipedia by Using Classical and Deep Learning ApproachesComputer Science – CACIC 201910.1007/978-3-030-48325-8_1(3-18)Online publication date: 14-May-2020
https://doi.org/10.1007/978-3-030-48325-8_1
Sciascio CStrohmaier DErrecalde MVeas E(2019)Interactive Quality Analytics of User-generated ContentACM Transactions on Interactive Intelligent Systems10.1145/31509739:2-3(1-42)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3150973
Lewoniewski WWęcel KAbramowicz W(2018)Determining Quality of Articles in Polish Wikipedia Based on Linguistic FeaturesInformation and Software Technologies10.1007/978-3-319-99972-2_45(546-558)Online publication date: 29-Aug-2018
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Khairova NLewoniewski WWęcel KOrken MKuralai M(2018)Comparative Analysis of the Informativeness and Encyclopedic Style of the Popular Web Information SourcesBusiness Information Systems10.1007/978-3-319-93931-5_24(333-344)Online publication date: 16-Jun-2018
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Lewoniewski WWęcel KAbramowicz W(2017)Relative Quality and Popularity Evaluation of Multilingual Wikipedia ArticlesInformatics10.3390/informatics40400434:4(43)Online publication date: 8-Dec-2017
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di Sciascio CStrohmaier DErrecalde MVeas EPapadopoulos GKuflik TChen FDuarte CFu W(2017)WikiLyzerProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025201(377-388)Online publication date: 7-Mar-2017
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Mining web sites using adaptive information extraction

Synonyms extraction using web content focused crawling

Using web information for author name disambiguation

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