This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph-based, considering the relationships among users to predict the spread information; human-based, using human perspectives to determine reliable sources; or machine learning-based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright (C) 2016 John Wiley & Sons, Ltd.