@incollection{ShahYahiaMcBrideetal., author = {Shah, Syed Attique and Yahia, Sadok Ben and McBride, Keegan and Jamil, Akhtar and Draheim, Dirk}, title = {Twitter Streaming Data Analytics for Disaster Alerts}, series = {2021 2nd International Informatics and Software Engineering Conference (IISEC)}, booktitle = {2021 2nd International Informatics and Software Engineering Conference (IISEC)}, publisher = {IEEE}, isbn = {978-1-6654-0759-5}, doi = {10.1109/IISEC54230.2021.9672370}, publisher = {Hertie School}, pages = {1 -- 6}, abstract = {In today's world, disasters, both natural and manmade, are becoming increasingly frequent, and new solutions are of a compelling need to provide and disseminate information about these disasters to the public and concerned authorities in an effective and efficient manner. One of the most frequently used ways for information dissemination today is through social media, and when it comes to real-time information, Twitter is often the channel of choice. Thus, this paper discusses how Big Data Analytics (BDA) can take advantage of information streaming from Twitter to generate alerts and provide information in real-time on ongoing disasters. The paper proposes TAGS (Twitter Alert Generation System), a novel solution for collecting and analyzing social media streaming data in realtime and subsequently issue warnings related to ongoing disasters using a combination of Hadoop and Spark frameworks. The paper tests and evaluates the proposed solution using Twitter data from the 2018 earthquake in Palu City, Sulawesi, Indonesia. The proposed architecture was able to issue alert messages on various disaster scenarios and identify critical information that can be utilized for further analysis. Moreover, the performance of the proposed solution is assessed with respect to processing time and throughput that shows reliable system efficiency.}, 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} } @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} } @article{Stockmann, author = {Stockmann, Daniela}, title = {Race to the Bottom: Media Marketization and Increasing Negativity Toward the United States in China}, series = {Political Communication in China: Convergence or Divergence Between the Media and Political System?}, volume = {28}, journal = {Political Communication in China: Convergence or Divergence Between the Media and Political System?}, number = {3}, publisher = {Routledge}, address = {London}, doi = {10.1080/10584609.2011.572447}, pages = {268 -- 290}, abstract = {This article examines how Chinese newspapers respond to opposing demands by audiences and Propaganda Department authorities about news regarding the United States when competition poses pressure on marketized media to make a profit. To examine the tone of news reporting about the U.S., I rely on a computer-aided text analysis of news stories published in the People's Daily and the Beijing Evening News, comparing the years 1999 and 2003 before and after the rise of commercialized newspapers in the Beijing newspaper market. Results show that the emergence of news competitors may exert pressure on less marketized papers to change news content, resulting in an increase of negative news about the United States. Evidence is provided to show that the rise of negative news is unlikely to result from an intended strategy by Propaganda authorities, actions undertaken by the American government, or journalists' own attitudes.}, language = {en} } @article{Stockmann, author = {Stockmann, Daniela}, title = {Responsive Authoritarianism in Chinese Media and Other Authoritarian Contexts}, series = {Political Communication Report}, volume = {25}, journal = {Political Communication Report}, number = {1}, language = {en} } @incollection{Stockmann, author = {Stockmann, Daniela}, title = {Information Overload? Collecting, Managing, and Analyzing Chinese Media Content}, series = {Contemporary Chinese Politics: New Sources, Methods, and Field Strategies}, booktitle = {Contemporary Chinese Politics: New Sources, Methods, and Field Strategies}, editor = {Carlson, Allen and Gallagher, Mary E. and Lieberthal, Kenneth and Manion, Melanie}, publisher = {Cambridge University Press}, address = {New York}, isbn = {9780521155762, 978-0521197830, 978-0521197830}, pages = {107 -- 128}, language = {mul} } @incollection{Stockmann, author = {Stockmann, Daniela}, title = {Media Influence on Ethnocentrism Towards Europeans}, series = {Chinese Views of the EU: Public Support for a Strong Relation}, booktitle = {Chinese Views of the EU: Public Support for a Strong Relation}, editor = {Dong, Lisheng and Wang, Zhengxu and Dekker, Henk}, publisher = {Routledge}, address = {London}, isbn = {9781136753244}, language = {en} } @article{Stockmann, author = {Stockmann, Daniela}, title = {One Size Doesn't Fit All: Measuring News Reception East and West}, series = {The Chinese Journal of Communication}, volume = {2}, journal = {The Chinese Journal of Communication}, number = {2}, doi = {10.1080/17544750902826640}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:b1570-opus4-23950}, pages = {140 -- 157}, abstract = {This article investigates how and why measures developed in the American context yield different results in China. Research in the United States has shown that a person's level of political knowledge is a stronger and more consistent predictor of news reception compared to alternative measures, such as media consumption or education. Yet a case study of news reception of pension reform in Beijing demonstrates that attentiveness and education constitute more valid indicators than knowledge. These differences in the empirical findings may result from translation from English into Chinese as well as specifics of the Chinese education system. However, when using valid measures the relationship between attentiveness and news reception is strong among Beijing residents, revealing that information-processing works as anticipated based on American media research.}, language = {en} } @incollection{Stockmann, author = {Stockmann, Daniela}, title = {The Chinese Internet Audience: Who Seeks Political Information Online?}, series = {Urban Mobilization and New Media in Contemporary China}, booktitle = {Urban Mobilization and New Media in Contemporary China}, editor = {Dong, Lisheng and Kriesi, Hanspeter and K{\"u}bler, Daniel}, publisher = {Ashgate}, address = {Farnham, UK and Burlington, VT}, isbn = {978-1-4724-3097-7}, language = {en} } @article{Stockmann, author = {Stockmann, Daniela}, title = {Towards Area-Smart Data Science: Critical Questions for Working with Big Data from China}, series = {Policy and Internet}, journal = {Policy and Internet}, number = {10(4)}, doi = {10.1002/poi3.192}, pages = {393 -- 414}, abstract = {While the Internet was created without much governmental oversight, states have gradually drawn territorial borders via Internet governance. China stands out as a promoter of such a territorial-based approach. China's separate Web infrastructure shapes data when information technologies capture traces of human behavior. As a result, area expertise can contribute to the substantive, methodological, and ethical debates surrounding big data. This article discusses how a number of critical questions that have been raised about big data more generally apply to the Chinese context: How does big data change our understanding of China? What are the limitations of big data from China? What is the context in which big data is generated in China? Who has access to big data and who knows the tools? How can big data from China be used in an ethical way? These questions are intended to spark conversations about best practices for collaboration between data scientists and China experts.}, language = {en} }