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Topics and topical phases in German social media communication during a disaster*

Published online by Cambridge University Press:  14 February 2018

SABINE GRÜNDER-FAHRER ,
ANTJE SCHLAF ,
GREGOR WIEDEMANN  and
GERHARD HEYER
SABINE GRÜNDER-FAHRER
Affiliation:
Institute for Applied Informatics and University of Leipzig, Natural Language Processing Group, Augustusplatz 10, 04109 Leipzig, Germany e-mails: gruender@uni-leipzig.de, antje.schlaf@informatik.uni-leipzig.de, gregor.wiedemann@uni-leipzig.de, heyer@informatik.uni-leipzig.de
ANTJE SCHLAF
Affiliation:
Institute for Applied Informatics and University of Leipzig, Natural Language Processing Group, Augustusplatz 10, 04109 Leipzig, Germany e-mails: gruender@uni-leipzig.de, antje.schlaf@informatik.uni-leipzig.de, gregor.wiedemann@uni-leipzig.de, heyer@informatik.uni-leipzig.de
GREGOR WIEDEMANN
Affiliation:
Institute for Applied Informatics and University of Leipzig, Natural Language Processing Group, Augustusplatz 10, 04109 Leipzig, Germany e-mails: gruender@uni-leipzig.de, antje.schlaf@informatik.uni-leipzig.de, gregor.wiedemann@uni-leipzig.de, heyer@informatik.uni-leipzig.de
GERHARD HEYER
Affiliation:
Institute for Applied Informatics and University of Leipzig, Natural Language Processing Group, Augustusplatz 10, 04109 Leipzig, Germany e-mails: gruender@uni-leipzig.de, antje.schlaf@informatik.uni-leipzig.de, gregor.wiedemann@uni-leipzig.de, heyer@informatik.uni-leipzig.de
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Abstract

Social media are an emerging new paradigm in interdisciplinary research in crisis informatics. They bring many opportunities as well as challenges to all fields of application and research involved in the project of using social media content for an improved disaster management. Using the Central European flooding 2013 as our case study, we optimize and apply methods from the field of natural language processing and unsupervised machine learning to investigate the thematic and temporal structure of German social media communication. By means of topic model analysis, we will investigate which kind of content was shared on social media during the event. On this basis, we will, furthermore, investigate the development of topics over time and apply temporal clustering techniques to automatically identify different characteristic phases of communication. From the results, we, first, want to reveal properties of social media content and show what potential social media have for improving disaster management in Germany. Second, we will be concerned with the methodological issue of finding and adapting natural language processing methods that are suitable for analysing social media data in order to obtain information relevant for disaster management. With respect to the first, application-oriented focal point, our study reveals high potential of social media content in the factual, organizational and psychological dimension of the disaster and during all stages of the disaster management life cycle. Interestingly, there appear to be systematic differences in thematic profile between the different platforms Facebook and Twitter and between different stages of the event. In context of our methodological investigation, we claim that if topic model analysis is combined with appropriate optimization techniques, it shows high applicability for thematic and temporal social media analysis in disaster management.

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Type
Articles
Information
Natural Language Engineering , Volume 24 , Issue 2 , March 2018 , pp. 221 - 264
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

*

The research leading to these results has received funding from the European community’s Seventh Framework Programme under grant agreement No. 607691 (SLANDAIL).

References

Backfried, G., Schmidt, C., and Quirchmayr, G., 2015. Cross-media linking in times of disaster. In Proceedings of the Information Systems for Crisis Response and Management (ISCRAM).Google Scholar
Baird, M. E. 2010. The phases of emergency management. Technical report, Vanderbilt Center for Transportation Research.Google Scholar
Baldwin, T. 2012. Social media: friend or foe of natural language processing? In Proceedings of the 26th Pacific Asia Conference on Language, Information and Computation (PACLIC 2012).Google Scholar
Blei, D. M., and Lafferty, J. D. 2006a. Correlated topic models. In Weiss, Y. and Schölkopf, B. and Platt, J. C. (eds.), Advances in Neural Information Processing Systems, pp. 147–154, MIT Press, Cambridge, MA.Google Scholar
Blei, D. M., and Lafferty, J. D., 2006b. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, pp. 113–20.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I., 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3 (1): 993–1022.Google Scholar
BMI 2008. Krisenkommunikation Leitfaden für Behörden und Unternehmen. Technical report, Bundesministerium des Inneren, Berlin.Google Scholar
Calinski, T., and Harabasz, J., 1974. A dendrite method for cluster analysis. Communications in Statistics 3 (1): 1–27.Google Scholar
Castillo, C., 2016. Big Crisis Data: Social Media in Disasters and Time-Critical Situations. New York: Cambridge University Press.CrossRefGoogle Scholar
Chowdhury, S. R., Imran, M., Amer-Yahia, S., Castillo, C., and Asghar, M. R., 2013. Tweet4act: Using incident-specific profiles for classifying crisis-related messages. In Proceedings of the 10th International ISCRAM Conference, pp. 1–5.Google Scholar
DKKV 2015. Das Hochwasser im Juni 2013 Bewährungsprobe für das Hochwasserrisikomanagement in Deutschland. Technical report, Deutsches Kommitee Katastrophenvorsorge, Bonn.Google Scholar
Dunning, T., 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics 19 (1): 61–74.Google Scholar
EIJK 2015. Kommunikationsfluten Tagung. http://konferenz.eijc.eu/konferenz-kommunikationsfluten.html (11.07.2016). European Institute for Journalism and Communication Research.Google Scholar
Eismann, K., Posegga, O., and Fischbach, K. 2016. Collective behaviour, social media, and disasters: a systematic literature review. In European Conference on Information Systems (ECIS), pp. 1–20.Google Scholar
Endres, D. M., and Schindelin, J. E., 2003. A new metric for probability distributions. IEEE Transactions on Information Theory 49 (7): 1858–60.CrossRefGoogle Scholar
Evans, M. S., 2014. A computational approach to qualitative analysis in large textual datasets. PloS One 9 (2): 1–10.CrossRefGoogle ScholarPubMed
Foster, J., Cetinoglu, O., Wagner, J., Le Roux, J., Nivre, J., Hogan, D., and van Genabith, J. 2011. From news to comment: resources and benchmarks for parsing the language of web 2.0. In Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP 2011), pp. 893–901.Google Scholar
Fuchs, G., Andrienko, N., Andrienko, G., Bothe, S., and Stange, H., 2013. Tracing the German centennial flood in the stream of tweets: first lessons learned. In SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information), pp. 2–10.CrossRefGoogle Scholar
Gad, S., Javed, W., Ghani, S., Elmqvist, N., Ewing, T., Hampton, K. N., and Ramakrishnan, N., 2015. ThemeDelta: dynamic segmentations over temporal topic models. IEEE Transactions on Visualization and Computer Graphics 21 (5): 672–85.CrossRefGoogle ScholarPubMed
Greene, D., O’Callaghan, D., and Cunningham, P. 2014. How many topics? Stability analysis for topic models. In Calders, T., Esposito, F., Hüllermeier, E., and Meo, R. (eds.), ECML PKDD 2014, pp. 498–513. Berlin: Springer.Google Scholar
Griffiths, T. L., and Steyvers, M., 2002. A probabilistic approach to semantic representation. In Proceedings of the 24th Annual Conference of the Cognitive Science Society.Google Scholar
Grün, O. 2014. Die Flutkatastrophe in Sachsen 2002. In Grün, O. and Schenker-Wicki, A. (eds.), Katastrophenmanagement: Grundlagen, Fallbeispiele und Gestaltungsoptionen aus betriebswirtschaftlicher Sicht. chapter 6, pp. 87–100. uniscope. Publikationen der SGO Stiftung. Wiesbaden: Springer Gabler.CrossRefGoogle Scholar
Gründer-Fahrer, S., and Schlaf, A., 2015. Modes of communication in social media for emergency management. In Proceedings of the 2nd Workshop on Computer Mediated Communication/Social Media at GSCL 2015, pp. 33–7.Google Scholar
Gründer-Fahrer, S., Schlaf, A., and Wustmann, S. 2018. How social media text analysis can inform disaster management. In Rehm, G. and Declerck, T. (eds.) Language Technologies for the Challenges of the Digital Age. GSCL 2017. Lecture Notes in Computer Science, vol 10713, pp. 199–207. Springer, Cham.Google Scholar
Hagar, C. 2006. Using research to aid the design of a crisis information management course. In Proceedings of the ALISE SIG Multicultural, Ethnic and Humanistic Concerns (MEH) session on Information Seeking and Service Delivery for Communities in Disaster/Crisis, San Antonio, Texas.Google Scholar
Hall, D., Jurafsky, D., and Manning, C. D., 2008. Studying the history of ideas using topic models. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP 08), pp. 363–71.CrossRefGoogle Scholar
Hughes, A. L., and Palen, L., 2009. Twitter adoption and use in mass convergence and emergency events. In Proceedings of the Information Systems for Crisis Response and Management (ISCRAM), pp. 248–60.CrossRefGoogle Scholar
Imran, M., Castillo, C., Diaz, F., and Vieweg, S., 2015. Processing social media messages in mass emergency: A survey. ACM Computing Surveys 47 (4): Article67.CrossRefGoogle Scholar
Imran, M., Elbassuoni, S. M., Castillo, C., Diaz, F., and Meier, P., 2013. Extracting information nuggets from disaster-related messages in social media. In Proceedings of the 10th International ISCRAM Conference, pp. 1–10.Google Scholar
Iyengar, A., Finin, T., and Joshi, A., 2011. Content-based prediction of temporal boundaries for events in Twitter. In Proceedings of the 3rd IEEE International Conference on Social Computing, pp. 186–91.CrossRefGoogle Scholar
Kaufhold, M.-A., and Reuter, C. 2016. The self-organization of digital volunteers across social media: The case of the 2013 European Floods in Germany. Journal of Homeland Security and Emergency Management 13 (1): 137–66.CrossRefGoogle Scholar
Kirchbachkommission 2013. Bericht der Kommission der Sächsischen Staatsregierung zur Untersuchung der Flutkatastrophe 2013 (Kirchbachbericht). Technical report, Sächsische Staatskanzlei, Dresden. www.publikationen.sachsen.de/bdb/artikel/20534; 25.01.2016.Google Scholar
Lancichinetti, A., Sirer, M. I., Wang, J. X., Acuna, D., Körding, K., and Amaral, A. N., 2015. High-reproducibility and high-accuracy method for automated topic classification. Physical Review X 5 (1): 11007.CrossRefGoogle Scholar
LfULG 2014. Ereignisanalyse Hochwasser Juni 2013. Technical report, Sächsisches Landesamt für Umwelt, Landwirtschaft und Geologie (LfULG), Dresden. https://publikationen.sachsen.de/bdb/artikel/15180; 11.07.2016.Google Scholar
LUBW, and LfU 2015. Länderübergreifendes Hochwasserportal - Hinweise. http://www.hochwasserzentralen.de/info.htm; 11.07.2016. Landesanstalt für Umwelt, Messungen und Naturschutz Baden-Württemberg (LUBW) und Bayerisches Landesamt für Umwelt (LfU).Google Scholar
McMahon, K. 2011. The Psychology of Disaster. Blog.Google Scholar
Mehrotra, R., Sanner, S., Buntine, W., and Xie, L., 2013. Improving LDA topic models for microblogs via tweet pooling and automatic labeling. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2013), pp. 889–92.CrossRefGoogle Scholar
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., and McCallum, A., 2011. Optimizing semantic coherence in topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 11), pp. 262–72.Google Scholar
Niekler, A. 2016. Automatisierte Verfahren für die Themenanalyse Nachrichtenorientierter Textquellen. PhD thesis, Faculty of Mathematics and Computer Science, University of Leipzig.Google Scholar
Olshannikova, E., Olsson, T., Huhtamäki, J., and Kärkkäinen, H. 2017. Conceptualizing big social data. Journal of Big Data 4 (1): 3.CrossRefGoogle Scholar
Olteanu, A., Castillo, C., Diaz, F., and Vieweg, S. 2014. CrisisLex: A lexicon for collecting and filtering microblogged communications in crises. In Proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM), pp. 376–85.Google Scholar
Olteanu, A., Vieweg, S., and Castillo, C. 2015. What to expect when the unexpected happens: Social media communications across crises. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 994–1009.Google Scholar
Palen, L., Anderson, K., Mark, G., Martin, J., Sicker, D., Palmer, M., and Grunwald, D., 2010. A vision for technology-mediated support for public participation & assistance in mass emergencies and disasters. In Proceedings of the ACM-BCS Visions of Computer Science.CrossRefGoogle Scholar
Palen, L., Vieweg, S., Liu, S. B., and Hughes, A. L. 2007. Crisis informatics: studying crisis in a networked world. In Proceedings of 3rd International Conference on e-Social Science, Ann Arbor, Michigan.Google Scholar
Pang, B., and Lee, L., 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2 (1–2): 1–135.CrossRefGoogle Scholar
Parsons, S., Atkinson, P. M., Simperl, E., and Weal, M. 2015. Thematically analysing social network content during disasters through the lens of the disaster management lifecycle. In Gangemi, A., Leonardi, S., and Panconesi, A., (eds.), WWW (Companion Volume), pp. 1221–6. ACM, Florence, Italy.CrossRefGoogle Scholar
Qu, Y., Huang, C., Zhang, P., and Zhang, J., 2011. Microblogging after a major disaster in China: a case study of the 2010 yushu earthquake. In Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work (CSCW 2011), pp. 25–34.CrossRefGoogle Scholar
QuOIMA 2011. QuOIMA Open Source Integrated Multimedia Analysis. www.kiras.at/projects, 08.06.2017.Google Scholar
Ramage, D., Dumais, S., and Liebling, D., 2010. Characterizing microblogs with topic models. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 130–7.CrossRefGoogle Scholar
Rayson, P., and Garside, R., 2000. Comparing corpora using frequency profiling. In Proceedings of the Workshop on Comparing Corpora, pp. 1–6.CrossRefGoogle Scholar
Rehu̇řek, R., and Sojka, P., 2010. Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45–55.Google Scholar
Remus, R., Quasthoff, U., and Heyer, G., 2010. Sentiws: a publicly available German-Language resource for sentiment analysis. In Proceedings of the 7th International Language Resources and Evaluation (LREC ’10), pp. 1168–71.Google Scholar
Reuter, C., Heger, O., and Pipek, V., 2013. Combining real and virtual volunteers through social media. In Proceedings of the Information Systems for Crisis Response and Management (ISCRAM), pp. 780–90.Google Scholar
Reuter, C., Ludwig, T., Kaufhold, M.-A., and Pipek, V., 2015. XHELP: design of a cross-platform social-media application to support volunteer moderators in disasters. In Proceedings of the Conference on Human Factors in Computing Systems (CHI), pp. 4093–102.CrossRefGoogle Scholar
Reuter, C., and Schröter, J., 2015. Microblogging during the European Floods 2013: what twitter may contribute in German Emergencies. International Journal of Information Systems for Crisis Response and Management 7 (1): 22–40.CrossRefGoogle Scholar
Reynolds, A. P., Richards, G., de la Iglesia, B., Beatriz, , and Rayward-Smith, V. J., 2006. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. Journal of Mathematical Modelling and Algorithms 5 (4): 475–504.CrossRefGoogle Scholar
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., and Steyvers, M., 2010. Learning author-topic models from text corpora. ACM Transactions on Information Systems 28 (1): 1–38.CrossRefGoogle Scholar
Rössler, P., 2005. Inhaltsanalyse. Konstanz: UVK Verlagsgesellschaft.Google Scholar
Scott, M., 1997. PC analysis of key words – and key key words. System 25 (1): 1–13.CrossRefGoogle Scholar
Sievert, C., and Shirley, K. E. 2014. Ldavis: a method for visualizing and interpreting topics. In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63–70, ACL, Baltimore.CrossRefGoogle Scholar
Slandail 2014. Slandail – Security System for language and image analysis. http://slandail.eu, 08.06.2017.Google Scholar
Starbird, K., and Palen, L., 2011. “Voluntweeters”: self-organizing by digital volunteers in times of crisis. In Proceedings of the ACM-BCS Visions of Computer Science.CrossRefGoogle Scholar
Starbird, K., and Palen, L., 2012. (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising. In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW), pp. 7–16.CrossRefGoogle Scholar
Starbird, K., Palen, L., Hughes, A. L., and Vieweg, S., 2010. Chatter on the red: what hazards threat reveals about the social life of microbloggs information. In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW), pp. 241–50.CrossRefGoogle Scholar
Steyvers, M., and Griffiths, T. L. 2005. Probabilistic topic models. In Landauer, T., McNamara, D., and Kintsch, W. (eds.), Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, pp. 427–448, Mawah, NJ.Google Scholar
Stieglitz, S., Dang-Xuan, L., Bruns, A., and Neuberger, C., 2014. Social media analytics – an interdisciplinary approach and its implications for information systems. Business & Information Systems Engineering 6 (2): 89–96.CrossRefGoogle Scholar
Temnikova, I., Castillo, C., and Vieweg, S. 2015. EMTerms 1.0: a terminological resource for crisis tweets. In Proceedings of the International Conference on Information Systems for Crisis Response and Management (ISCRAM), pp. 134–46.Google Scholar
Vieweg, S. 2012. Situational Awareness in Mass Emergency: A Behavioral and Linguistic Analysis of Microblogged Communications. PhD thesis, University of Colorado at Boulder.Google Scholar
Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L., 2010. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems.CrossRefGoogle Scholar
Wang, C., Blei, D. M., and Heckerman, D., 2008. Continuous time dynamic topic models. In Proceedings of the Conference on Uncertainty in Artificial Intelligence, pp. 579–86.Google Scholar
Wang, Y., Agichtein, E., and Benzi, M. 2012. TM-LDA: efficient online modeling of latent topic transitions in social media. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 123–31.Google Scholar
Wiedemann, G., 2016. Text Mining for Qualitative Data Analysis in the Social Sciences. A Study on Democratic Discourse in Germany. Wiesbaden: Springer VS (Kritische Studien zur Demokratie).Google Scholar
Yang, S., Chung, H., Lin, X., Lee, S., Chen, L., Wood, A., Kavanaugh, A. L., Sheetz, S. D., Shoemaker, D. J., and Fox, E. A., 2013. PhaseVis: what, when, where, and who in visualizing the four phases of emergency management through the lens of social media. In Proceedings of the 10th International ISCRAM Conference, pp. 912–18.Google Scholar
Zhao, W. X., Jing, J., Weng, J. J., He, J., Lim, E.-P., Yan, H., and Li, X., 2011. Comparing twitter and traditional media using topic models. In Proceedings of 33rd European Conference on IR Research (ECIR 2011), pp. 338–49.CrossRefGoogle Scholar