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This article presents the use of second-order adaptive network models of hospital teams consisting of doctors and nurses, interacting together. A variety of scenarios are modelled and simulated, in relation with respiratory distress of a neonate, along with the integration of an AI-Coach for monitoring and support of such teams and of organizational learning. The research highlights the benefits of introducing a virtual AI-Coach in a hospital setting. The practical application setting revolves around a medical team responsible for managing neonates with respiratory distress. In this setting an AI-Coach act as an additional team member, to ensure correct execution of medical procedure. Through simulation experiments, the adaptive network models demonstrate the progression of the scenarios and highlight how the diverse challenges that may arise throughout the processes are handled.

Social media have given a dimension that is beyond any geographical limits, which is growing tremendously. It has been useful in providing real-time communication opportunities; however, its massive usage has its own pitfalls. This thesis aims to address two possible human behaviors in the context of social media usage, i.e., aggression and narcissism. The deeper impact of each of these behaviors is studied by designing mathematical models, simulating them based on multidisciplinary literature, and verifying them by applying analysis and machine learning techniques. Each behavior is modeled through computational network-based modeling, which uses the multidisciplinary literature available. These models are causal by nature and indicate the factors that can lead to such behaviors. After simulating these models, their behavior has been validated empirically using qualitative empirical information from the literature and real-world data, for example, by analyzing conversational tweets through Language Processing or analyzing quantitative questionnaire data. While discussing the aggressors and narcissists, the thesis also presents computational models for the sufferer of these behaviors, along with the possible regulation to feel better and supported. During the analysis of data, the dynamics of the designed models were studied in particular. All models were declarative by nature and were studied mostly in combination with the analysis of the longitudinal qualitative data collected from social media over time, or by the analysis of quantitative data from surveys. Thus, this thesis provides a significant contribution to the state of the art, by providing a sound basis for modeling and predicting specific negative behaviors on social media by performing data analytics, which can be extended to study these behaviors in-depth and to provide related support.

Social media have given a dimension that is beyond any geographical limits, which is growing tremendously. It has been useful in providing real-time communication opportunities; however, its massive usage has its own pitfalls. This thesis aims to address two possible human behaviors in the context of social media usage, i.e., aggression and narcissism. The deeper impact of each of these behaviors is studied by designing mathematical models, simulating them based on multidisciplinary literature, and verifying them by applying analysis and machine learning techniques. Each behavior is modeled through computational network-based modeling, which uses the multidisciplinary literature available. These models are causal by nature and indicate the factors that can lead to such behaviors. After simulating these models, their behavior has been validated empirically using qualitative empirical information from the literature and real-world data, for example, by analyzing conversational tweets through Language Processing or analyzing quantitative questionnaire data. While discussing the aggressors and narcissists, the thesis also presents computational models for the sufferer of these behaviors, along with the possible regulation to feel better and supported. During the analysis of data, the dynamics of the designed models were studied in particular. All models were declarative by nature and were studied mostly in combination with the analysis of the longitudinal qualitative data collected from social media over time, or by the analysis of quantitative data from surveys. Thus, this thesis provides a significant contribution to the state of the art, by providing a sound basis for modeling and predicting specific negative behaviors on social media by performing data analytics, which can be extended to study these behaviors in-depth and to provide related support.

Social media like Twitter or Instagram play a role of fertile ground for self-exhibition, which is used by various narcissists to share their frequent updates reflecting their narcissism. Their belief of saving and assisting others, make them vulnerable to the feedback of others, so their rage is as dangerous as their messiah complex. In this paper, we aim to analyse the behaviour of a narcissist when he is admired or receives negative critics. We designed a complex adaptive mental network model of the process of narcissism based on the theories of neuroscience and psychology including a Hebbian learning principal. The model was validated by analyzing Instagram data.