A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History. Dahm, E., Schubotz, M., Meuschke, N., & Gipp, B. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 1627–1634, April, 2017. ACM. Venue Rating: CORE A*![A Vision for Performing Social and Economic Data Analysis using Wikipedia's Edit History [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex In this vision paper, we suggest combining two lines of research to study the collective behavior of Wikipedia contributors. The first 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 profiles of user groups. User profiles 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.
@inproceedings{DahmSMG17,
title = {A {Vision} for {Performing} {Social} and {Economic} {Data} {Analysis} using {Wikipedia}'s {Edit} {History}},
isbn = {978-1-4503-4914-7},
url = {https://www.gipp.com/wp-content/papercite-data/pdf/dahm2017.pdf},
doi = {10.1145/3041021.3053363},
abstract = {In this vision paper, we suggest combining two lines of research to study the collective behavior of Wikipedia contributors. The first 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 profiles of user groups. User profiles 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.},
booktitle = {Proceedings of the 26th {International} {Conference} on {World} {Wide} {Web} {Companion}},
publisher = {ACM},
author = {Dahm, Erik and Schubotz, Moritz and Meuschke, Norman and Gipp, Bela},
month = apr,
year = {2017},
note = {Venue Rating: CORE A*},
keywords = {Miscellaneous},
pages = {1627--1634},
}
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