@incollection{DariusStephany, author = {Darius, Philipp and Stephany, Fabian}, title = {How the Far-Right Polarises Twitter: 'Hashjacking' as a Disinformation Strategy in Times of COVID-19}, series = {Complex Networks \& Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016}, booktitle = {Complex Networks \& Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-93413-2}, doi = {10.1007/978-3-030-93413-2_9}, publisher = {Hertie School}, pages = {100 -- 111}, abstract = {Twitter influences political debates. Phenomena like fake news and hate speech show that political discourses on social platforms can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of 'hashjacking'(The use of someone else's hashtag in order to promote one's own social media agenda.). For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative f{\"u}r Deutschland (AfD) and their potential use of 'hashjacking' in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by 'hashjacking' COVID-19 related hashtags, like \#CoronaVirusDE or \#FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.}, language = {en} } @article{StephanyNeuhaeuserStoehretal., author = {Stephany, Fabian and Neuh{\"a}user, Leonie and Stoehr, Niklas and Darius, Philipp and Teutloff, Ole and Braesemann, Fabian}, title = {The CoRisk-Index: A Data-Mining Approach to Identify Industry-Specific Risk Perceptions Related to Covid-19}, series = {Humanities and Social Sciences Communications}, volume = {9}, journal = {Humanities and Social Sciences Communications}, number = {1}, doi = {10.1057/s41599-022-01039-1}, abstract = {The global spread of Covid-19 has caused major economic disruptions. Governments around the world provide considerable financial support to mitigate the economic downturn. However, effective policy responses require reliable data on the economic consequences of the corona pandemic. We propose the CoRisk-Index: a real-time economic indicator of corporate risk perceptions related to Covid-19. Using data mining, we analyse all reports from US companies filed since January 2020, representing more than a third of the US workforce. We construct two measures—the number of 'corona' words in each report and the average text negativity of the sentences mentioning corona in each industry—that are aggregated in the CoRisk-Index. The index correlates with U.S. unemployment rates across industries and with an established market volatility measure, and it preempts stock market losses of February 2020. Moreover, thanks to topic modelling and natural language processing techniques, the CoRisk data provides highly granular data on different dimensions of the crisis and the concerns of individual industries. The index presented here helps researchers and decision makers to measure risk perceptions of industries with regard to Covid-19, bridging the quantification gap between highly volatile stock market dynamics and long-term macroeconomic figures. For immediate access to the data, we provide all findings and raw data on an interactive online dashboard.}, language = {en} } @unpublished{DariusStephany, author = {Darius, Philipp and Stephany, Fabian}, title = {How the Far-Right Polarises Twitter: 'Highjacking' Hashtags in Times of COVID-19}, doi = {10.31235/osf.io/n6f3r}, abstract = {Twitter influences political debates. Phenomena like fake news and hate speech show that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Some political actors actively employ strategies to facilitate polarisation on Twitter, as past contributions show, via strategies of 'hashjacking'. For the example of COVID-19 related hashtags and their retweet networks, we examine the case of partisan accounts of the German far-right party Alternative f{\"u}r Deutschland (AfD) and their potential use of 'hashjacking' in May 2020. Our findings indicate that polarisation of political party hashtags has not changed significantly in the last two years. We see that right-wing partisans are actively and effectively polarising the discourse by 'hashjacking' COVID-19 related hashtags, like \#CoronaVirusDE or \#FlattenTheCurve. This polarisation strategy is dominated by the activity of a limited set of heavy users. The results underline the necessity to understand the dynamics of discourse polarisation, as an active political communication strategy of the far-right, by only a handful of very active accounts.}, language = {en} } @techreport{StephanyStoehrDariusetal., type = {Working Paper}, author = {Stephany, Fabian and Stoehr, Niklas and Darius, Philipp and Neuh{\"a}user, Leonie and Teutloff, Ole and Braesemann, Fabian}, title = {The CoRisk-Index: A data-mining approach to identify industry-specific risk assessments related to COVID-19 in real-time}, series = {General Economics (econ.GN)}, journal = {General Economics (econ.GN)}, abstract = {While the coronavirus spreads, governments are attempting to reduce contagion rates at the expense of negative economic effects. Market expectations plummeted, foreshadowing the risk of a global economic crisis and mass unemployment. Governments provide huge financial aid programmes to mitigate the economic shocks. To achieve higher effectiveness with such 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 industry-specific risks related to COVID-19. We examine company risk reports filed to the U.S. Securities and Exchange Commission (SEC). This alternative data set can complement more traditional economic indicators in times of the fast-evolving crisis as it 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. Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing, we identify corona-related risk topics and their perceived relevance for different industries. The preliminary findings are summarised as an up-to-date online index. The CoRisk-Index tracks the industry-specific risk assessments related to the crisis, as it spreads through the economy. The tracking tool is updated weekly. It could provide relevant empirical data to inform models on the economic effects of the crisis. Such complementary empirical information could ultimately help policymakers to effectively target financial support in order to mitigate the economic shocks of the crisis.}, language = {en} } @incollection{DariusStephany, author = {Darius, Philipp and Stephany, Fabian}, title = {"Hashjacking" the Debate: Polarisation Strategies of Germany's Political Far-Right on Twitter}, series = {Social Informatics. SocInfo 2019. Lecture Notes in Computer Science, vol 11864}, volume = {11846}, booktitle = {Social Informatics. SocInfo 2019. Lecture Notes in Computer Science, vol 11864}, editor = {Weber (et al.), Ingmar}, publisher = {Springer}, address = {Cham}, issn = {978-3-030-34971-4}, doi = {10.1007/978-3-030-34971-4_21}, pages = {298 -- 308}, abstract = {Twitter is a digital forum for political discourse. The emergence of phenomena like fake news and hate speech has shown that political discourse on micro-blogging can become strongly polarised by algorithmic enforcement of selective perception. Recent findings suggest that some political actors might employ strategies to actively facilitate polarisation on Twitter. With a network approach, we examine the case of the German far-right party Alternative f{\"u}r 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.}, language = {en} }