What makes a good biography?
Multidimensional quality analysis based on Wikipedia article feedback data
In: IW3C2 (Hrsg.): Proceedings of the 23rd International World Wide Web Conference (WWW 2014)
International World Wide Web Conferences Steering Committee
URL des Volltextes:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings
World wide web 2.0
With more than 22 million articles, the largest collaborative knowledge resource never sleeps, experiencing several article edits every second. Over one fifth of these articles describes individual people, the majority of which are still alive. Such articles are, by their nature, prone to corruption and vandalism. Manual quality assurance by experts can barely cope with this massive amount of data. Can it be effectively replaced by feedback from the crowd? Can we provide meaningful support for quality assurance with automated text processing techniques? Which properties of the articles should then play a key role in the machine learning algorithms and why? In this paper, we study the user-perceived quality of Wikipedia articles based on a novel Wikipedia user feedback dataset. In contrast to previous work on quality assessment which mostly relied on judgements of active Wikipedia authors, we analyze ratings of ordinary Wikipedia users along four quality dimensions (complete, well written, trustworthy and objective). We first present an empirical analysis of the novel dataset with over 36 million Wikipedia article ratings. We then select a subset of biographical articles and perform classification experiments to predict their quality ratings along each of the dimensions, exploring multiple linguistic, surface and network properties of the rated articles. Additionally, we study the classification performance and differences for the biographies of living and dead people as well as those for men and women. We demonstrate the effectiveness of our approach by the F1 scores of 0.94, 0.89, 0.73, and 0.73 for the dimensions complete, well written, trustworthy, and objective. Based on the results, we believe that the quality assessment of big textual data can be effectively supported by current text classification and language processing tools.