Addressing False Information and Abusive Language in Digital Space Using Intelligent Approaches

While digital space is a place where users communicate increasingly, the recent threat of COVID-19 infection even more emphasised the necessity of effective and well-organised online environment Therefore, it is nowadays, more whenever in the past, important to deal with various unhealthy phenomena, that prohibit effective communication and knowledge sharing in the digital space Undesired user behaviour and user-generated content in the online environment (mostly on social media) can have various forms, probably, the most harmful is the creation and spreading of false information (e g , fake news) and using abusive language (e g hate speech) While a notable amount of research effort has been already dedicated to reducing and mitigating the negative consequences of such phenomena, a number of additional challenges and open problems remain unsolved In this book chapter, at first, we provide a summary of existing research works, challenges and open problems Consequently, we introduce our research results addressing false information and abusive language Our approaches are based on intelligent and knowledge-based methods, mainly machine learning, natural language processing, and semi-automatic approaches Besides the detection of undesired content, we present also less studied approaches to identify users, who contribute or spread such content Our approaches are also complemented by the methods that are specific for computational social science and humanities as hyperlink network analysis, conceptual analysis, and interviews Finally, we present some of our applications for monitoring and mitigating such undesired content and behaviour © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG