Forgiveness and trust dynamics on social networks. (April 2018)

• Record Type: Journal Article
• Authors: Laifa, Meriem; Akrouf, Samir; Mammeri, Ramdane
• Is Part Of: Adaptive behavior. Volume 26:Number 2(2018)
• Page Start: 65
• Page End: 83
• Publication Date: 2018-04
• Subjects: Forgiveness -- trust -- fuzzy logic -- artificial neural networks -- structural equation modeling -- simulation; Animal behavior -- Periodicals; Animals -- Adaptation -- Periodicals; Adaptability (Psychology) -- Periodicals; Adaptation, Psychological -- Periodicals; Artificial intelligence -- Periodicals; 591.5
• DOI: 10.1177/1059712318762733
• Languages: English
• ISSNs: 1741-2633

Abstract

Social media users can easily be offended or hurt on those platforms, which leads to discomfort and health issues such as stress and anxiety. Forgiveness plays an important role to maintain healthy online relationships, which is the central constituent of social dynamics, from cooperation to social cohesion. While most prior studies have focused on analyzing forgiveness factors in offline settings using statistical methods, this study offers a new perspective using a two-staged approach whereby a research model was tested using structural equation modeling (SEM), and then the results were used as inputs for artificial neural network (ANN) and fuzzy logic (FL) models. An agent-based simulation was then performed to shed light on a possible use of the implemented models. Combining ANN and FL provided more accurate prediction results. In addition, simulation experiments call attention to the potential benefits of forgiveness in maintaining connectedness in a social network. The main purpose of this investigation was to evaluate the applicability of soft computing techniques on forgiveness prediction. Instead of relying on data mining techniques, we looked into questions that can improve our understanding of how society works in a digital age. In addition, this study provides an interesting example of a different and insightful way of doing computational social science that is useful to both researchers and practitioners.