A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History

Dahm, Erik; Schubotz, Moritz; Meuschke, Norman; Gipp, Bela

A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History

Dateien

Dahm_2-1l512au51xto19.pdfGröße: 1.04 MBDownloads: 350

Datum

2017

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

BARRETT, Rick, ed.. WWW'17 Companion : Proceedings of the 26th International Conference on World Wide Web. New York, NY, USA: ACM Press, 2017, pp. 1627-1634. ISBN 978-1-4503-4914-7. Available under: doi: 10.1145/3041021.3053363

Zusammenfassung

In this vision paper, we suggest combining two lines of research to study the collective behavior of Wikipedia contributors. The rst line of research analyzes Wikipedia's edit history to quantify the quality of individual contributions and the resulting reputation of the contributor. The second line of research surveys Wikipedia contributors to gain insights, e.g., on their personal and professional background, socioeconomic status, or motives to contribute toWikipedia. While both lines of research are valuable on their own, we argue that the combination of both approaches could yield insights that exceed the sum of the individual parts. Linking survey data to contributor reputation and content-based quality metrics could provide a large-scale, public domain data set to perform user modeling, i.e. deducing interest pro les of user groups. User pro les can, among other applications, help to improve recommender systems. The resulting dataset can also enable a better understanding and improved prediction of high quality Wikipedia content and successfulWikipedia contributors. Furthermore, the dataset can enable novel research approaches to investigate team composition and collective behavior as well as help to identify domain experts and young talents. We report on the status of implementing our large-scale, content-based analysis of the Wikipedia edit history using the big data processing framework Apache Flink. Additionally, we describe our plans to conduct a survey among Wikipedia contributors to enhance the content-based quality metrics.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Wikipedia; Author Reputation; Article Quality; Editor Types

Konferenz

26th International Conference on World Wide Web, 3. Apr. 2017 - 7. Apr. 2017, Perth, Australia

Zitieren

ISO 690

DAHM, Erik, Moritz SCHUBOTZ, Norman MEUSCHKE, Bela GIPP, 2017. A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History. 26th International Conference on World Wide Web. Perth, Australia, 3. Apr. 2017 - 7. Apr. 2017. In: BARRETT, Rick, ed.. WWW'17 Companion : Proceedings of the 26th International Conference on World Wide Web. New York, NY, USA: ACM Press, 2017, pp. 1627-1634. ISBN 978-1-4503-4914-7. Available under: doi: 10.1145/3041021.3053363

BibTex

@inproceedings{Dahm2017Visio-41870,
  year={2017},
  doi={10.1145/3041021.3053363},
  title={A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History},
  isbn={978-1-4503-4914-7},
  publisher={ACM Press},
  address={New York, NY, USA},
  booktitle={WWW'17 Companion : Proceedings of the 26th International Conference on World Wide Web},
  pages={1627--1634},
  editor={Barrett, Rick},
  author={Dahm, Erik and Schubotz, Moritz and Meuschke, Norman and Gipp, Bela}
}

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Universitätsbibliographie

Ja