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Computational Infodemiology is a novel discipline that maps the indicators that underpin digital discourse. Being able to fundamentally map belief systems within a scale free network and provide a framework by which the evolution of belief can be mapped, outlines the impact uncertainty has on an echo chamber and models the spread of misinformation. This book provides the framework and empirical data to outline these scenarios.
To map the impact of uncertainty, certain indicators need to be tracked, and within the scale-free network, social stress indicators are introduced. These social stress indicators serve as a proxy for potential harm and can differentiate between low, medium and high levels of harm. By incorporating social stress indicators, belief system mapping becomes viable and possible to model misinformation spread within an echo chamber.
List of contents
Preface.- Chapter 1 Introduction to Computational Infodemiology.- Chapter 2 Agent Based Modelling and Reinforcement Learning in Computational Infodemiology.- Chapter 3 A Framework for Modelling Belief Systems.- Chapter 4 Modelling Belief Systems.- Chapter 5 Social Stress Indicators.
About the author
Dr. Herkulaas M. V. E Combrink is a Senior Lecturer and Co-Director of the Interdisciplinary Centre for Digital Futures, University of the Free State. Dr Combrink specializes in infodemiology, computational infodemiology, human language technology, and artificial intelligence. Dr Combrink has contributed significantly to data science for social impact by combining data analytics, data science, artificial intelligence, health, education, and policy for better societal outcomes. Dr Combrink continues to innovate in the fields of Computational Infodemiology, Social Stress and Void-Learning. He was the PI of a multi-institutional Human Language Technology project funded by the Department of Sport, Arts and Culture, an international project funded by the British Academy, and National Funding through the Struwig Cancer Trust.
Summary
Computational Infodemiology is a novel discipline that maps the indicators that underpin digital discourse. Being able to fundamentally map belief systems within a scale free network and provide a framework by which the evolution of belief can be mapped, outlines the impact uncertainty has on an echo chamber and models the spread of misinformation. This book provides the framework and empirical data to outline these scenarios.
To map the impact of uncertainty, certain indicators need to be tracked, and within the scale-free network, social stress indicators are introduced. These social stress indicators serve as a proxy for potential harm and can differentiate between low, medium and high levels of harm. By incorporating social stress indicators, belief system mapping becomes viable and possible to model misinformation spread within an echo chamber.
