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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.