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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\u2019s 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.<\/jats:p>","DOI":"10.1177\/0165551519877646","type":"journal-article","created":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T23:15:29Z","timestamp":1569885329000},"page":"176-191","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["A deep learning-based quality assessment model of collaboratively edited documents: A case study of Wikipedia"],"prefix":"10.1177","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0033-4150","authenticated-orcid":false,"given":"Ping","family":"Wang","sequence":"first","affiliation":[{"name":"Center for the Studies of Information Resources, School of Information Management, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Duke University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renli","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Management, Wuhan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"bibr1-0165551519877646","volume-title":"Analyzing and predicting quality flaws in user-generated content: the case of Wikipedia","author":"Anderka M","year":"2013"},{"key":"bibr2-0165551519877646","first-page":"11","volume-title":"Proceedings of the 2nd Joint WICOW\/AIRWeb workshop on web quality","author":"Anderka M"},{"key":"bibr3-0165551519877646","first-page":"464","volume-title":"Proceedings of the 2015 IEEE\/ACM international conference on advances in social networks analysis and mining","author":"de la Robertie B"},{"key":"bibr4-0165551519877646","first-page":"582","volume-title":"Proceedings of 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