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The paper presents results of an experiment conducted in 2013 via Amazon Mechanical Turk Platform (www.mturk.com) aimed at creating a classifier predicting online content credibility evaluation misjudgement tendencies. The rough sets based module processes demographic variables describing each participant and predicts his/her misjudgement tendency. Data collection method, data-set preparation are described in detail. Next the rough set methodology is introduced explaining the process of training and validating using available data. Experimental results are presented in detail showing the classification accuracy for various configurations of rough-sets algorithms. The analysis of importance of subsequent demographic variables on prediction efficiency is discussed as well. The paper is concluded with future prospects and future applications of implemented methodology.

Analysis of Demographical Factors’ Influence on Websites’ Credibility Evaluation