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The proximity between newspapers and political parties is strongly subjective and difficult to measure. Yet, political tendencies of newspapers can have a significant impact on voters’ opinion‐forming and ought to be known by the public in a transparent and timely manner. This article introduces the Sentiment Political Compass (SPC), a data‐driven framework for analyzing political bias of newspapers toward political parties. Using the SPC, newspapers are embedded in a two‐dimensional space (left‐leaning vs. right‐leaning, libertarian vs. autocratic). To assess the informative value of our framework, we crawled a data set consisting of 180,000 newspaper articles from twenty‐five newspapers during the German Federal Elections over a time period of 18 months and extracted 740,000 political entities enriched with their contextual sentiment. We analyze this dataset on the party‐ and politician‐level as well as considering the temporal dimension and draw insights about the relationship between newspapers and political parties. We provide the data set and our code open‐source at http://www.politicalcompass.de to encourage the application of the SPC to other political landscapes.

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.

To understand the dynamics of the digital knowledge economy, it is crucial to reveal the geography of global flows of knowledge on digital platforms. This article visualizes a key form of knowledge production in the digital economy: mapping the joint collaborations of users from different cities on Stack Overflow, the world's most popular question-and-answer website for programming questions. The network map reveals that users from only a limited number of places are actively taking part in the exchange of programming knowledge. While Stack Overflow access and participation are theoretically unrestricted, contributions are clustered in metropolitan regions in North America, Western Europe, and South Asia.

In common law, the outcome of a new case is determined mostly by precedent cases, rather than by existing statutes. However, how exactly does the precedent influence the outcome of a new case? Answering this question is crucial for guaranteeing fair and consistent judicial decision-making. We are the first to approach this question computationally by comparing two longstanding jurisprudential views; Halsbury's, who believes that the arguments of the precedent are the main determinant of the outcome, and Goodhart's, who believes that what matters most is the precedent's facts. We base our study on the corpus of legal cases from the European Court of Human Rights (ECtHR), which allows us to access not only the case itself, but also cases cited in the judges' arguments (i.e. the precedent cases). Taking an information-theoretic view, and modeling the question as a case outcome classification task, we find that the precedent's arguments share 0.38 nats of information with the case's outcome, whereas precedent's facts only share 0.18 nats of information (i.e., 58% less); suggesting Halsbury's view may be more accurate in this specific court. We found however in a qualitative analysis that there are specific statues where Goodhart's view dominates, and present some evidence these are the ones where the legal concept at hand is less straightforward.