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Abstract
Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.
Type
Conference Contribution
Type of thesis
Series
Citation
Granatyr, J., Gomes, H. M., DIas, J. M., Paiva, A. M., Nunes, M. A. S. N., Scalabrin, E. E., & Spak, F. (2019). Inferring trust using personality aspects extracted from texts. In Proceeding of IEEE International Conference on Systems, Man and Cybernetics (SMC 2019) (pp. 3840–3846). Washington, DC, USA: IEEE. https://doi.org/10.1109/SMC.2019.8914641
Date
2019
Publisher
IEEE
Degree
Supervisors
Rights
This is an author’s accepted version of an article published in the Proceeding of IEEE International Conference on Systems, Man and Cybernetics (SMC 2019). © 2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.