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Wikipedia is a free-content Internet encyclopedia that can be edited by anyone who accesses it. As a result, Wikipedia contains both featured and non-featured articles. Featured articles are high-quality articles and nonfeatured articles are poor quality articles. Since there is an exponential growth of Wikipedia articles, the need to identify the featured Wikipedia articles has become indispensable so as to provide quality information to the users. As very few attempts have been carried out in the biology domain of English Wikipedia articles, we present our study to automatically measure the information quality in biological Wikipedia articles. Since the coherence shows representational information quality of a text, we have used the discourse connective count measure for our study. We compare this novel measure with two other popular approaches word count measure and explicit document model method that have been successfully applied to the task of quality measurement in Wikipedia articles. We organized the Wikipedia articles into balanced and unbalanced set. The balanced set contains featured and non-featured articles of equal length and the unbalanced set contains randomly selected featured and non-featured articles. The best result for the balanced set is obtained with F-measure of 83.2%, while using Support Vector Machine classifier with 4-gram representation and Term Frequency-Inverse Document Frequency weighting scheme. Meanwhile, the best result for unbalanced corpus is obtained using the discourse connective count measure with an F measure of 98.06%.

Discourse Connective - A Marker for Identifying Featured Articles in Biological Wikipedia