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The study explores differences between three user types in the top tweets about the 2015 “refugee crisis” in Germany and presents the results of a quantitative content analysis. All tweets with the keyword “Flüchtlinge” posted for a monthlong period following September 13, 2015, the day Germany decided to implement border controls, were collected (N = 763,752). The top 2,495 tweets according to number of retweets were selected for analysis. Differences between news media, public and private actor tweets in topics, tweet characteristics such as tone and opinion expression, links, and specific sentiments toward refugees were analyzed. We found strong differences between the tweets. Public actor tweets were the main source of positive sentiment toward refugees and the main information source on refugee support. News media tweets mostly reflected traditional journalistic norms of impartiality and objectivity, whereas private actor tweets were more diverse in sentiments toward refugees.
Der statistische Diskussionbeitrag untersucht, ob und wie sich Erwartungen und Stimmungen in der Wirtschaft bilden bzw. von welchen volkswirtschaftlichen Größen sie abhängen. Als Methodik werden Partial Least Squares (PLS) Modelle genutzt, eine Modellklasse der Pfadanalyse mit latenten Variablen. Die verwendeten Daten wurden vom Ifo-Institut und aus der amtlichen Statistik entnommen.