SentiTrust: a new Trust Model for Decentralized Online Social Media

Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users....

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Guidi, Barbara, Michienzi, Andrea, Ricci, Laura, Baiardi, Fabrizio, Gomez-Zaragoza, Lucia, Carrasco-Ribelles, Lucia A., Marin-Morales, Javier
Format: Article
Language:English
Subjects:
Analytical models
Arousal
Artificial intelligence
Data mining
Decentralized Online Social Networks
Digital media
Electronic mail
Multimedia
Online Social Networks
Peer-to-peer computing
Privacy
Sentiment analysis
Social networking (online)
Social networks
statistical learning
Trust
Trustworthiness
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Summary:Online Social Media (OSM) are dominating the wide range of Internet services. Due to their vast audience, it is crucial to evaluate the interpersonal trust among OSM users that can identify reliable sources of information, the meaningfulness of a relationship, or the trustworthiness of other users. SentiTrust is an innovative trust model for Decentralized Online Social Networks that is based on AI-powered Sentiment Analysis. It enriches the trust definition by exploiting important features that are enabled because of the adoption of Social Media through mobile devices. The model can be easily extended and customized according to the scenario of interest. The sentiment analysis component has been tested by involving 30 participants who completed several guided tasks using a social media while their electrodermal activity and rate responses were measured. The results suggest that low arousal states are related to receiving happy faces and to sending more messages per minute. Furthermore, positive interactions result in shorter interactions and multimedia exchange.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3281194