Mutual evaluation of editors and texts for assessing quality of Wikipedia articles

Abstract

In this paper, we propose a method to identify good quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing article quality is a text survival ratio based approach. In this approach, when a text survives beyond multiple edits, the text is assessed as good quality. This approach assumes that poor quality texts are deleted by editors with high possibility. However, many vandals delete good quality texts frequently, then the survival ratios of good quality texts are improperly decreased by vandals. As a result, many good quality texts are unfairly assessed as poor quality. In our method, we consider editor quality for calculating text quality, and decrease the impacts on text qualities by the vandals who has low quality. Using this improvement, the accuracy of the text quality should be improved. However, an inherent problem of this idea is that the editor qualities are calculated by the text qualities. To solve this problem, we mutually calculate the editor and text qualities until they converge. We did our experimental evaluation, and we confirmed that the proposed method could accurately assess the text qualities.

Measuring Quality of Collaboratively Edited Documents: The Case of Wikipedia

“…NDCG metric proposed by Jarvelin et al [58] to evaluate ranking systems was used by several studies on the quality assessment of Wikipedia articles [10], [59], [60]. The ranking is done according to the quality of the articles from high to low, i.e.…”

Escalation of TRUST analysis in web

“…Study has also been done to identify high quality content automatically in question-and-answer networks using numerous appropriately relative and inherent features [3]. The similar kind of work for the social media can be found in [4] and for Wikipedia in [5]. In [6] and [7], a system named as TRELLIS is developed that allows users to make trust related ratings about sources (entities) based on the content provided.…”

Effects of Implicit Positive Ratings for Quality Assessment of Wikipedia Articles

“…Lim et al [11], [12] and Suzuki et al [13] proposed a method to calculate quality values using this model. In these methods, the system generates a graph where the nodes corresponds to the editors, and the edges are correspond to the amount of contribution, and then analyzes the graph for calculating quality values.…”