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While the coronavirus spreads around the world, governments are attempting to reduce contagion rates at the expense of negative economic effects.Market expectations have plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the expected economic shocks. To achieve higher effectiveness with cyclical and fiscal policy measures, it is key to identify the industries that are most in need of support.In this study, we introduce a data-mining approach to measure the industryspecific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This data set allows for a real-time analysis of risk assessments. Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments by weeks. The risk reports differ substantially between industries, both in magnitude and in nature. Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries. Our approach allows to distinguish the industries by their reported risk awareness towards COVID-19.The preliminary findings are summarised an online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool could provide relevant empirical data to inform models on the immediate economic effects of the crisis. Such complementary empirical information could help policymakers to effectively target financial support and to mitigate the economic shocks of the current crisis.

With a network approach, we examine the case of the German far-right party Alternative für Deutschland (AfD) and their potential use of a "hashjacking" strategy - the use of someone else’s hashtag in order to promote one's own social media agenda. Our findings suggest that right-wing politicians (and their supporters/retweeters) actively and effectively polarise the discourse not just by using their own party hashtags, but also by "hashjacking" the political party hashtags of other established parties. The results underline the necessity to understand the success of right-wing parties, online and in elections, not entirely as a result of external effects (e.g. migration), but as a direct consequence of their digital political communication strategy.