Refine
Year of publication
Document Type
- Report (7)
- Article (5)
- Working Paper (2)
- Conference Proceeding (1)
Has Fulltext
- yes (15) (remove)
Is part of the Bibliography
- no (15) (remove)
Keywords
- Twitter (15) (remove)
Institute
- Gesellschaftswissenschaften (7)
- Exzellenzcluster Die Herausbildung normativer Ordnungen (3)
- Center for Financial Studies (CFS) (2)
- Geschichtswissenschaften (2)
- Sustainable Architecture for Finance in Europe (SAFE) (2)
- Wirtschaftswissenschaften (2)
- Akademie für Bildungsforschung und Lehrerbildung (bisher: Zentrum für Lehrerbildung und Schul- und Unterrichtsforschung) (1)
- House of Finance (HoF) (1)
- Institute for Monetary and Financial Stability (IMFS) (1)
- Neuere Philologien (1)
We assemble a data set of more than eight million German Twitter posts related to the war in Ukraine. Based on state-of-the-art methods of text analysis, we construct a daily index of uncertainty about the war as perceived by German Twitter. The approach also allows us to separate this index into uncertainty about sanctions against Russia, energy policy and other dimensions. We then estimate a VAR model with daily financial and macroeconomic data and identify an exogenous uncertainty shock. The increase in uncertainty has strong effects on financial markets and causes a significant decline in economic activity as well as an increase in expected inflation. We find the effects of uncertainty to be particularly strong in the first months of the war.
As kindergartens and schools closed down during the first wave of the COVID-19 pandemic in Germany, two hashtags emerged on Twitter: #CoronaEltern (#CoronaParents) and #CoronaElternRechnenAb (#CoronaParentsDocumentTheCosts). In this paper, we examine the positioning practices around both hashtags as expressions of “digital activism” (Joyce 2010: VIII). One characteristic of the hashtag campaign is that political demands are hardly ever made directly. Rather, the participants resort to five main linguistic patterns: (1) they address different target groups; (2) they refer to different protagonists; (3) in the subcorpus #CoronaEltern specifically, they constitute themselves as a collective through (4) the recurring use of first-person narratives; (5) and generalization and typification. Our findings show that #CoronaParents are not just parents in times of a pandemic: #CoronaParents are only those who see themselves as such, participating in an evolving, at times misunderstood community.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
In this article, we hypothesize, and then demonstrate, that experiences of embarrassment have significantly increased in the United States, due in part, to the current situation in American politics under President Donald Trump. We provide support for our hypothesis by conducting both qualitative and quantitative analyses of Twitter posts in the U.S. obtained from the Crimson Hexagon database. Next, based on literature from social psychology, social neuroscience, and political theory, we propose a two-step process explaining why Trump's behavior has caused people in the U.S. to feel more embarrassment. First, compared to former representatives, Trump violates social norms in a manner that seems intentional, and second, these intentional norm violations specifically threaten the social integrity of in-group members—in this case, U.S. citizens. We discuss how these norm violations relate to the behavior of currently represented citizens and contextualize our rationale in recent changes of political representation and the public sphere. We conclude by proposing that more frequent, nation-wide experiences of embarrassment on behalf of the representative may motivate political actions to prevent further harm to individuals' self-concepts and protect social integrity.
Die re:publica 2018 in der Twitteranalyse: User Statistiken, beliebteste Tweets und insbesondere die Debatte um das Verhalten der Bundeswehr rund um die #rp18. Deskriptive Analysen und rudimentäres Textmining. Agenda-Setting durch die Bundeswehr? Vielleicht ein bisschen. For our international readers, the graphs are kept in english. R code and data here.
Last week’s printed edition of Focus had a piece about how Germany’s politicians are using social media. It made the dubious claim that 61% of Green top candidate Katrin Göring-Eckardt’s Twitter followers could have been bought.
Let’s actually instead try to get to grips with what is going on here, and try to draw some conclusions. ...
Last week, this year’s ISA conference brought together over 5000 scholars and exhibitors from all over the world to discuss all things international, political, scholarly, hold meetings, get lunch together, and party at Mardi Gras (it was in New Orleans, after all!). Similar to last year, a lot of this discussing took also place on Twitter. Scholars-slash-tweeps rallied around the hashtag #isa2015 to talk to each other online about great (and not so great) panels, trends in IR scholarship, gender bias in academia, and (not surprisingly for an academic conference) coffee. Who was most active during ISA2015 on Twitter? What were the most hotly debated topics online? When did ISAlers tweet?