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"This book is a practical guide on how to write conceptual papers and use conceptual generalization as a research methodology. Divided into two parts, the book first focuses on the scientific foundation for conceptual generalization, to identify what is a conceptual model and how conceptual models can be developed. Part two focuses on how to write a winning conceptual thesis, covering conceptual generalisation and empirical generalisation, and discusses research problems and questions, and how to analyse them. The authors cover different conceptual and analytical models to offer students a multitude of tools to visualize, interpret and uncover relationships and patterns. For example, they explore the thought experiment, analytical models, empirical causal models, analytical forms and data mining models, and outline a strategy for developing conceptual models to assist with students who wish to design their own conceptual paper. Students gain a clear understanding of the driving forces in the research process, how to define a research problem, how to analysis the problem and develop research strategies. Moving from concepts to hypotheses, the book also covers the main types of errors that may be encountered as students learn about understanding the development of models and how to develop a theory. Also including a checklist for students, and a list of definitions and concepts, this is the ideal resource for advanced undergraduate and postgraduate students, and researchers, in the social sciences"--

Master's thesis in Information systems (IS501) ; Fake news has,inrecentyears,gained traction in the public media and as a research topic. Events such as the U.S 2016 presidential election, Brexit,the COVID-19 pandemic, amongst others,have seen tracesof large amounts offake news in social media. Social media sites like Twitter have enabled individuals, politicians,and companies to sharecontent and opinions witha large numberof peopleacross the globe. This opportunityfor mass communication has also ledtoTwitter becoming a place for fake news sharing. Various narratives by various actors partakein the same public discussions,andknowing whatis true and whatis fake is increasingly difficult. The purpose of this study wastoexamine and analyze a previously not studied dataset of 14.3 million tweets related to the 2016 refugee crisisand attemptto find traces of fake news. Theresearch approachchosenwas an exploratory case studywith mixed data analysis.The analyzed focusedon findingthe characteristicsof tweets, the most prominent topics,identifyingfake news,some of the actors(webpages) spreading fake news,and classify the type of fake news.To identify what content was fake, an extensive amount of literature in combination with three fact-checking services were utilized.