Forgiveness and trust dynamics on social networks

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.

Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

“…An agent's network position provides social capital which leads to certain achievements, success or power. Examples include the evolution of cooperation based on familiarity (Son & Rojas, 2011), similarities (Hadzibeganovic et al, 2018) or trust (Bravo et al, 2012;Growiec et al, 2018;Laifa et al, 2018). Additionally, a social environment can provide existential security (Gore et al, 2018) or can support people to promote an activity (Garcia et al, 2018).…”

“…We observed, however, that this has rarely been used in ABM so far. Examples for co-evolutionary networks comprise agents that add or remove links to maximize the information they can gain from their acquaintances (Frank et al, 2018;Lozano et al, 2018;Moradianzadeh et al, 2018;Phan & Godes, 2018), to establish monogamous mating relationships (Simão & Todd, 2002), to express their dissatisfaction within a cooperation (Bravo et al, 2012) or if the trust between agents has vanished due to offenses between neighbours (Laifa et al, 2018). Additionally, modified spatial configurations that emerge from the behaviour of the agents (e.g.…”

“…Global network measures map network properties to a single value (Araújo & Banisch, 2016), thus modifying them also changes the entire network. SNA provides several metrics at this level such as network density (Chica et al, 2018;Growiec et al, 2018;Kaufmann et al, 2009;Phan & Godes, 2018) and size (Chen et al, 2012;Janssen & Jager, 2001;Ke et al, 2008;Laifa et al, 2018) or the rewiring probability in smallworld networks (Baggio & Hillis, 2018;Bohlmann et al, 2010;Janssen & Jager, 2001, 2003. In addition, the global clustering coefficient, network diameter and average path length fall under this category.…”

A Survey on Trust Prediction in Online Social Networks

“…They incorporated temporal dynamics to model the dynamics of users' preferences. Laifa et al [129] tested a research model using structural equation modeling and delivered the outputs to an artificial neural network and fuzzy logic model developing their dynamic prediction approach. Liu and Datta [130] designed another dynamic trust prediction approach.…”

Mamdani fuzzy rule-based models for psychological research

“…Many researchers [6,7,13,16,18,19,29,30] proposed integration of fuzzy logic in psychological research for more logical outcomes. Recently Devi [5] and Laifa [15] integrated fuzzy set theory with neural network to develop intelligent models for psychological research.…”