Visible to the public Inferring Trust Using Personality Aspects Extracted from Texts

TitleInferring Trust Using Personality Aspects Extracted from Texts
Publication TypeConference Paper
Year of Publication2019
AuthorsGranatyr, Jones, Gomes, Heitor Murilo, Dias, João Miguel, Paiva, Ana Maria, Nunes, Maria Augusta Silveira Netto, Scalabrin, Edson Emílio, Spak, Fábio
Conference Name2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
Date Publishedoct
Keywordsaffective aspects, Computational modeling, Correlation, data mining, Data models, data privacy, decision making, evaluation system, Expert Systems and Privacy, Human Behavior, human factors, inference mechanisms, logical protection, malicious people, nonpersonality-wise approaches, Numerical models, online human-fed evaluation systems, personality models, psychology, pubcrawl, Scalability, security of data, Software systems, text analysis, Tools, trust computation, trust inference, trust mechanisms, trust model, Trusted Computing
AbstractTrust 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.
DOI10.1109/SMC.2019.8914641
Citation Keygranatyr_inferring_2019