Narcissism and fame:a complex network model for the adaptive interaction of digital narcissism and online popularity

Social media like Twitter or Instagram play the role of fertile platforms for self-exhibition and allow their users to earn a good repute. People higher in grandiosity share their contents in a charismatic way and as a result, they are successful in gaining attention from others, which may also influence their responses and behaviors. Such attention and repute enable them to be a trendsetter or a socially recognized maven. In this paper, we present a complex adaptive mental network model of a narcissist to see how popularity can adaptively influence his/her behavior. To analyze and to support behavior showed by our model, we used some key performance indicators from the literature to study the popularity and narcissism of 30 Instagram profiles. The results of the—both computational and empirical—study indicate that our presented computational adaptive network model in general shows the behavior found from the empirical data

Modelling learning for a better safety culture within an organization using a virtual safety coach:Reducing the risk of postpartum depression via improved communication with parents

This paper describes an extension of a safety culture within hospital organizations providing more transparency and acknowledgement of all actors, and in particular the parents. It contributes a model architecture to support a hospital to develop such an extended safety culture. It is illustrated for prevention of postpartum depression. Postpartum depression is a commonly known consequence of childbirth for both mothers and fathers. In this research, we computationally analyze the risk factors and lack of support received by fathers. Therefore, we use shared mental models to model the effects of poor and additional communication by healthcare practitioners to mitigate the development of postpartum depression in both the mother and the father. Both individual mental models and shared mental models are considered in the design of the computational model. The paper illustrates the benefits of simple support in terms of communication during childbirth, which has lasting effects, even outside the hospital. For the impact of additional communication, a Virtual Safety Coach is designed that intervenes when necessary to provide support, i.e., when a health care practitioner doesn't. Moreover, organizational learning is also modelled to improve the mental models of both the Safety Coach and the Health Care Practitioner.Safety and Security Scienc

DARK SIDE OF THE DIGITAL WORLD: Computational Analysis of Negative Human Behaviors on Social Media

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

How to make process model matching work better? An analysis of current similarity measures

\u3cp\u3eProcess model matching techniques aim at automatically identifying activity correspondences between two process models that represent the same or similar behavior. By doing so, they provide essential input for many advanced process model analysis techniques such as process model search. Despite their importance, the performance of process model matching techniques is not yet convincing and several attempts to improve the performance have not been successful. This raises the question of whether it is really not possible to further improve the performance of process model matching techniques. In this paper, we aim to answer this question by conducting two consecutive analyses. First, we review existing process model matching techniques and give an overview of the specific technologies they use to identify similar activities. Second, we analyze the correspondences of the Process Model Matching Contest 2015 and reflect on the suitability of the identified technologies to identify the missing correspondences. As a result of these analyses, we present a list of three specific recommendations to improve the performance of process model matching techniques in the future.\u3c/p\u3

An adaptive network model for AI-assisted monitoring and management of neonatal respiratory distress

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 that the AI-Coach not only aids in maintaining correct medical procedure execution but also facilitates organizational learning, leading to significant improvements in procedure adherence and error reduction during neonatal care.</p

Healing the next generation:an adaptive agent model for the effects of parental narcissism

Parents play an important role in the mental development of a child. In our previous work, we addressed how a narcissistic parent influences a child (online/offline) when (s)he is happy and admires the child. Now, we address the influence of a parent who is not so much pleased, and may curse the child for being the reason for his or her unhappiness. An abusive relationship with a parent can also cause trauma and poor mental health of the child. We also address how certain coping behaviors can help the child cope with such a situation. Therefore, the aim of the study is threefold. We present an adaptive agent model of a child, while incorporating the concept of mirroring through social contagion, the avoidance behaviors from a child, and the effects of regulation strategies to cope with stressful situations

How happy you are: A computational study of social impact on self-esteem

People take the opportunity from social-networking sites like Facebook, to express themselves in various communities. Various studies address that it has influence over self-esteem of its users. In this paper, we analyse how the self-esteem of a person is affected in the computational world. To accomplish this, we built a computational model of esteem. A questionnaire-based survey was conducted to collect data for model verification. Different simulation experiments were conducted to compare and evaluate the model concerning findings from the literature and data. This model can be used as a useful input to provide support to people who are influenced by negative feedback