Problematizing and addressing the article-as-concept assumption in Wikipedia

Yilun Lin; Bowen Yu; Andrew Hall; Brent Hecht

doi:10.1145/2998181.2998274

Problematizing and addressing the article-as-concept assumption in Wikipedia

Yilun Lin, Bowen Yu, Andrew Hall, Brent Hecht

Communication Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Scopus citations

Abstract

Wikipedia-based studies and systems frequently assume that no two articles describe the same concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editors' tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. "Portland, Oregon" and "History of Portland, Oregon" in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the subarticle matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.

Original language	English (US)
Title of host publication	CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
Publisher	Association for Computing Machinery
Pages	2052-2067
Number of pages	16
ISBN (Electronic)	9781450343350
DOIs	https://doi.org/10.1145/2998181.2998274
State	Published - Feb 25 2017
Event	2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 - Portland, United States Duration: Feb 25 2017 → Mar 1 2017

Publication series

Name	Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Other

Other	2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
Country/Territory	United States
City	Portland
Period	2/25/17 → 3/1/17

Funding

The authors would like to thank Stephanie Hernandez, Geovanna Hinojoza, Federico Peredes, Stephanie Hecht, Patti Bao, and Darren Gergle for their valuable contributions to this project. This project was funded by NSF IIS-1552955, NSF IIS-1526988, and NSF IIS-1421655.

Keywords

Peer production
Sub-article matching problem
Wikipedia

ASJC Scopus subject areas

Software
Computer Networks and Communications
Human-Computer Interaction

Access to Document

10.1145/2998181.2998274

Cite this

Lin, Y., Yu, B., Hall, A., & Hecht, B. (2017). Problematizing and addressing the article-as-concept assumption in Wikipedia. In CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (pp. 2052-2067). (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW). Association for Computing Machinery. https://doi.org/10.1145/2998181.2998274

Lin, Yilun ; Yu, Bowen ; Hall, Andrew et al. / Problematizing and addressing the article-as-concept assumption in Wikipedia. CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery, 2017. pp. 2052-2067 (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW).

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abstract = "Wikipedia-based studies and systems frequently assume that no two articles describe the same concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editors' tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. {"}Portland, Oregon{"} and {"}History of Portland, Oregon{"} in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the subarticle matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.",

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Lin, Y, Yu, B, Hall, A & Hecht, B 2017, Problematizing and addressing the article-as-concept assumption in Wikipedia. in CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, Association for Computing Machinery, pp. 2052-2067, 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017, Portland, United States, 2/25/17. https://doi.org/10.1145/2998181.2998274

Problematizing and addressing the article-as-concept assumption in Wikipedia. / Lin, Yilun; Yu, Bowen; Hall, Andrew et al.
CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery, 2017. p. 2052-2067 (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Lin Y, Yu B, Hall A, Hecht B. Problematizing and addressing the article-as-concept assumption in Wikipedia. In CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery. 2017. p. 2052-2067. (Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW). doi: 10.1145/2998181.2998274

Problematizing and addressing the article-as-concept assumption in Wikipedia

Abstract

Publication series

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Funding

Keywords

ASJC Scopus subject areas

Access to Document

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