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Forgiveness and trust dynamics on social networks

机译：社交网络上的宽恕和信任动态

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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.
机译：社交媒体用户在这些平台上很容易受到冒犯或伤害，从而导致不适和健康问题，例如压力和焦虑。宽恕对于维持健康的在线关系起着重要的作用，从合作到社会凝聚力，在线关系是社会动力的核心组成部分。尽管大多数先前的研究都集中于使用统计方法分析离线环境中的宽恕因素，但本研究使用两阶段方法提供了新的视角，其中使用结构方程模型（SEM）对研究模型进行了测试，然后将结果用作人工神经网络（ANN）和模糊逻辑（FL）模型的输入。然后执行了基于代理的模拟，以阐明实现模型的可能用途。结合ANN和FL提供了更准确的预测结果。此外，模拟实验提醒人们注意宽恕在维持社交网络中的连接性方面的潜在好处。这项研究的主要目的是评估软计算技术在宽恕预测上的适用性。我们不再依赖数据挖掘技术，而是研究可以改善我们对数字时代社会运作方式的理解的问题。此外，本研究提供了一个有趣的例子，说明了进行社会科学计算的不同而有见地的方法，这对研究人员和从业人员均有用。

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