Measuring article quality in wikipedia: models and evaluation Export

@inproceedings{citeulike:2358120, abstract = {Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our Basic model is designed based on the mutual dependency between article quality and their author authority. The PeerReview model introduces the review behavior into measuring article quality. Finally, our ProbReview models extend PeerReview with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.}, address = {New York, NY, USA}, author = {Hu, Meiqun and Lim, Ee-Peng and Sun, Aixin and Lauw, Hady W. and Vuong, Ba-Quy}, booktitle = {CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management}, citeulike-article-id = {2358120}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1321476}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1321440.1321476}, citeulike-linkout-2 = {http://pages.cs.wisc.edu/~baquy/papers/cikm07.pdf}, comment = {Classifier takes features such as number of edits, num of words per edit, num authors per article, num reviewers per article and tries to predict quality of Wikipedia articles. Quality was assigned manually by Wikipedia according to their quality assessment scale. ---=note-separator=--- "Our evaluation and analysis in this paper have been focusing on article quality rankings for collaboratively authored content."}, doi = {10.1145/1321440.1321476}, isbn = {9781595938039}, keywords = {abuse, article, classification, classifier, collaboration, collaborative-authoring, country, dataset, edits, empirical, encyclopedia, peer-review, prediction, quality, quality-assessment, quality-assurance, rigor, wikipedia}, pages = {243--252}, posted-at = {2008-07-25 00:43:35}, priority = {0}, publisher = {ACM}, title = {Measuring article quality in wikipedia: models and evaluation}, url = {http://dx.doi.org/10.1145/1321440.1321476}, year = {2007} }

TY - CONF ID - citeulike:2358120 L3 - citeulike-article-id:2358120 N2 - Wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. Despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of Wikipedia articles edited by non-experts and inexperienced contributors. In this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of Wikipedia. We propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. Our Basic model is designed based on the mutual dependency between article quality and their author authority. The PeerReview model introduces the review behavior into measuring article quality. Finally, our ProbReview models extend PeerReview with partial reviewership of contributors as they edit various portions of the articles. We conduct experiments on a set of well-labeled Wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement. SN - 9781595938039 AD - New York, NY, USA EP - 252 TI - Measuring article quality in wikipedia: models and evaluation PB - ACM T2 - CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management SP - 243 KW - abuse KW - article KW - classification KW - classifier KW - collaboration KW - collaborative-authoring KW - country KW - dataset KW - edits KW - empirical KW - encyclopedia KW - peer-review KW - prediction KW - quality KW - quality-assessment KW - quality-assurance KW - rigor KW - wikipedia N1 - Classifier takes features such as number of edits, num of words per edit, num authors per article, num reviewers per article and tries to predict quality of Wikipedia articles. Quality was assigned manually by Wikipedia according to their quality assessment scale. N1 - "Our evaluation and analysis in this paper have been focusing on article quality rankings for collaboratively authored content." AU - Hu, Meiqun AU - Lim, Ee-Peng AU - Sun, Aixin AU - Lauw, Hady AU - Vuong, Ba-Quy PY - 2007/// UR - http://dx.doi.org/10.1145/1321440.1321476 ER -