A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia. (April 2021)

• Record Type: Journal Article
• Authors: Wang, Ping; Li, Xiaodan; Wu, Renli
• Is Part Of: Journal of information science. Volume 47:Number 2(2021)
• Page Start: 176
• Page End: 191
• Publication Date: 2021-04
• Subjects: Deep learning -- feature framework -- information quality assessment -- Wikipedia; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551519877646
• Languages: English
• ISSNs: 0165-5515

Abstract

Wikipedia is becoming increasingly critical in helping people obtain information and knowledge. Its leading advantage is that users can not only access information but also modify it. However, this presents a challenging issue: how can we measure the quality of a Wikipedia article? The existing approaches assess Wikipedia quality by statistical models or traditional machine learning algorithms. However, their performance is not satisfactory. Moreover, most existing models fail to extract complete information from articles, which degrades the model's performance. In this article, we first survey related works and summarise a comprehensive feature framework. Then, state-of-the-art deep learning models are introduced and applied to assess Wikipedia quality. Finally, a comparison among deep learning models and traditional machine learning models is conducted to validate the effectiveness of the proposed model. The models are compared extensively in terms of their training and classification performance. Moreover, the importance of each feature and the importance of different feature sets are analysed separately.