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

A Vision for Performing Social and Economic Data Analysis Using Wikipedia's Edit History - scientific work related to Wikipedia quality published in 2017, written by Erik Dahm, Moritz Schubotz, Norman Meuschke and Bela Gipp.

Overview

In this vision paper, authors 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, authors 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. Authors report on the status of implementing large-scale, content-based analysis of the Wikipedia edit history using the big data processing framework Apache Flink. Additionally, authors describe plans to conduct a survey among Wikipedia contributors to enhance the content-based quality metrics.