This paper discusses an approach to modeling and measuring information quality of Wikipedia articles. The approach is based on the idea that the quality of Wikipedia articles with distinctly different profiles needs to be measured using different information quality models. We report on our initial study, which involved two categories of Wikipedia articles: "stabilized" (those, whose content has not undergone major changes for a significant period of time) and "controversial" (the articles, which have undergone vandalism, revert wars, or whose content is subject to internal discussions between Wikipedia editors). We present simple information quality models and compare their performance on a subset of Wikipedia articles with the information quality evaluations provided by human users. Our experiment shows, that using special-purpose models for information quality captures user sentiment about Wikipedia articles better than using a single model for both categories of articles.

This work discusses an approach to modeling and measuring information quality of Wikipedia articles. The approach is based on the idea that the quality of Wikipedia articles with distinctly different profiles needs to be measured using different information quality models. To implement this approach, a software framework written in the Java language was developed to collect and analyze information of Wikipedia articles.We report on our initial study, which involved two categories of Wikipedia articles: "stabilized" (those, whose content has not undergone major changes for a significant period of time) and "controversial" (articles that have undergone vandalism, revert wars, or whose content is subject to internal discussions between Wikipedia editors). In addition, we present simple information quality models and compare their performance on a subset of Wikipedia articles with the information quality evaluations provided by human users. Our experiment shows that using special-purpose models for information quality captures user sentiment about Wikipedia articles better than using a single model for both categories of articles.iv ACKNOWLEDGEMENTS