Abstract
Wikipedia has become the most popular on-line encyclopedia. Millions of users rely on it to obtain desired knowledge and thus it becomes important and practical to model the quality of Wikipedia articles and to have inferior contents which bother readers or even mislead readers to be predicted. While identifying low-quality articles with manual efforts is a possible solution, it costs too much manpower and is too time-consuming. In this paper, we utilize article ratings from Wikipedia users for the first time to assess article quality. We define “low-quality” based on those ratings and design automatic methods to identify potential low-quality articles. More specifically, we formulate the problem as a set of binary classification problems and label articles according to whether they are “low-quality”. We compare two baseline algorithms and Logistic Regression algorithm, and the results indicate that it is promising to design effective and efficient automatic solutions for the task. We believe that our work is important for ensuring the quality of Wikipedia, as well as other knowledge markets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
References
Adler, B., de Alfaro, L., & Pye, I. (2010). Detecting Wikipedia vandalism using WikiTrust. Notebook papers of CLEF, pp. 22–23.
Adler, B. T., de Alfaro, L., Mola-Velasco, S. M., Rosso, P., & West, A. G. (2011). Wikipedia vandalism detection: Combining natural language, metadata, and reputation features. In Computational linguistics and intelligent text processing, pp. 277–288. (Springer).
Anderka, M., Stein, B., & Lipka, N. (2012). Predicting quality flaws in user-generated content: The case of Wikipedia. Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, pp. 981–990. (ACM).
Blumenstock, J. E. (2008). Size matters: Word count as a measure of quality on Wikipedia. Proceedings of the 17th international conference on World Wide Web, pp. 1095–1096. (ACM).
Hosmer, D. W. and Lemeshow, S. (2004). Applied logistic regression (Vol. 354). New York: Wiley.
Hu, M., Lim, E.-P., Sun, A., Lauw, H. W., & Vuong, B.-Q. (2007). Measuring article quality in Wikipedia: Models and evaluation. Proceedings of the sixteenth ACM conference on information and knowledge management, pp. 243–252. (ACM).
Potthast, M., Stein, B., & Gerling, R. (2008). Automatic vandalism detection in Wikipedia. InAdvances in information retrieval, pp. 663–668. (Springer).
Smets, K., Goethals, B., & Verdonk, B. (2008). Automatic vandalism detection in Wikipedia: Towards a machine learning approach. AAAI workshop on Wikipedia and artificial intelligence: An Evolving Synergy, pp. 43–48.
Stvilia, B., Twidale, M. B., Smith, L. C., & Gasser, L. (2005). Assessing information quality of a community-based encyclopedia. Proceedings of the international conference on information quality, pp. 442–454.
Wilkinson, D. M., & Huberman, B. A. (2007). Cooperation and quality in Wikipedia. Proceedings of the 2007 international symposium on Wikis, pp. 157–164. (ACM).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Zhang, N., Ruan, L., Si, L. (2015). Predicting Low-Quality Wikipedia Articles Using User’s Judgements. In: Bertino, E., Matei, S. (eds) Roles, Trust, and Reputation in Social Media Knowledge Markets. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-05467-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-05467-4_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05466-7
Online ISBN: 978-3-319-05467-4
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)