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Microblogging During the European Floods 2013: What Twitter May Contribute in German Emergencies

Microblogging During the European Floods 2013: What Twitter May Contribute in German Emergencies

Christian Reuter, Julian Schröter
Copyright: © 2019 |Pages: 21
ISBN13: 9781522561958|ISBN10: 1522561951|EISBN13: 9781522561965
DOI: 10.4018/978-1-5225-6195-8.ch034
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MLA

Reuter, Christian, and Julian Schröter. "Microblogging During the European Floods 2013: What Twitter May Contribute in German Emergencies." Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 739-759. https://doi.org/10.4018/978-1-5225-6195-8.ch034

APA

Reuter, C. & Schröter, J. (2019). Microblogging During the European Floods 2013: What Twitter May Contribute in German Emergencies. In I. Management Association (Ed.), Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications (pp. 739-759). IGI Global. https://doi.org/10.4018/978-1-5225-6195-8.ch034

Chicago

Reuter, Christian, and Julian Schröter. "Microblogging During the European Floods 2013: What Twitter May Contribute in German Emergencies." In Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 739-759. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-6195-8.ch034

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Abstract

Social media is becoming more and more important in crisis management. However its analysis by emergency services still bears unaddressed challenges and the majority of studies focus on the use of social media in the USA. In this paper German tweets of the European Flood 2013 are therefore captured and analyzed using descriptive statistics, qualitative data coding, and computational algorithms. The authors' work illustrates that this event provided sufficient German traffic and geo-locations as well as enough original data (not derivative). However, up-to-date Named Entity Recognizer (NER) with German classifier could not recognize German rivers and highways satisfactorily. Furthermore the authors' analysis revealed pragmatic (linguistic) barriers resulting from irony, wordplay, and ambiguity, as well as in retweet-behavior. To ease the analysis of data they suggest a retweet ratio, which is illustrated to be higher with important tweets and may help selecting tweets for mining. The authors argue that existing software has to be adapted and improved for German language characteristics, also to detect markedness, seriousness and truth.

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