Deutsch
Bibtex
Cite as text
@Select Types{,
Journal = "Band-2",
Title= "The Usage of Twitter Data for Early Crisis Detection",
Author= "Milad Mirbabaie, Stefan Stieglitz, and Leonie Lambertz ",
Doi= "https://doi.org/10.30844/wi_2020_v1-mirbabaie",
Abstract= "The establishment of novel information and communications technologies, such as social media, has changed how individuals and organizations behave. Particularly during uncertain situations caused by natural or man-made disasters, people communicate and share information via social media. Emergency services could use published content of social media participants for an early detection of crisis situations and thus enhance crisis management. However, exploiting these information bears several challenges. To gain insights from large amounts of unstructured data in time-critical situations, emergency services are in the need of tools that enable them to gather, process, and analyze relevant information. In this research article, we follow the design science paradigm and propose a concept (artifact) of an early crisis detection tool. Based on expert interviews, design implications for the concept tool are derived. In addition, we conducted a design thinking workshop to evaluate the artifact, highlighting its practical relevance and usability.
",
Keywords= "Crisis Management, Crisis Communication, Social Media, Early Warning System",
}
Milad Mirbabaie, Stefan Stieglitz, and Leonie Lambertz: The Usage of Twitter Data for Early Crisis Detection. Online: https://doi.org/10.30844/wi_2020_v1-mirbabaie (Abgerufen 23.11.24)
Open Access
Abstract
Abstract
The establishment of novel information and communications technologies, such as social media, has changed how individuals and organizations behave. Particularly during uncertain situations caused by natural or man-made disasters, people communicate and share information via social media. Emergency services could use published content of social media participants for an early detection of crisis situations and thus enhance crisis management. However, exploiting these information bears several challenges. To gain insights from large amounts of unstructured data in time-critical situations, emergency services are in the need of tools that enable them to gather, process, and analyze relevant information. In this research article, we follow the design science paradigm and propose a concept (artifact) of an early crisis detection tool. Based on expert interviews, design implications for the concept tool are derived. In addition, we conducted a design thinking workshop to evaluate the artifact, highlighting its practical relevance and usability.
Keywords
Schlüsselwörter
Crisis Management, Crisis Communication, Social Media, Early Warning System
References
Referenzen
1. Lindsay, B.R.: Social Media and Disasters: Current Uses, Future Options and Policy Consifderations. (2011).
2. Brynielsson, J., Granåsen, M., Lindquist, S., Narganes Quijano, M., Nilsson, S., Trnka, J.: Informing crisis alerts using social media: best practices and proof of concept. J. Contingencies Cris. Manag. 1–13 (2017).
3. Spielhofer, T., Greenlaw, R., Markham, D., Hahne, A.: Data mining Twitter during the UK floods: Investigating the potential use of social media in emergency management. In: Proceedings of the 3rd ICT-DM. IEEE, Vienna, Austria (2016).
4. Lee, N.-S., Hirschmeier, S., Müller, S., Luz, L.J.: Enablers in Crisis Information Management: A Literature Review. In: HICSS. pp. 274–283 (2017).
5. Panagiotopoulos, P., Barnett, J., Bigdeli, A.Z., Sams, S.: Social media in Emergency Management: Twitter as a Tool for Communicating Risks to the Public. Technol. Forecast. Soc. Change. 111, 86–96 (2016).
6. Wendling, C., Radisch, J., Jacobzone, S.: The Use of Social Media in Risk and Crisis Communication. (2013).
7. Karl, I., Nestler, S.: Nutzung von sozialen Medien als bürgerzentriertes Frühwarnsystem für Krisensituationen. In: Koch, M., Butz, A., and Schlichter, J. (eds.) Mensch und Computer 2014 – Workshopband. pp. 125–130.
8. Singh, J.P., Dwivedi, Y.K., Rana, N.P., Kumar, A., Kapoor, K.K.: Event classification and location prediction from tweets during disasters. Ann. Oper. Res. 1–21 (2017).
9. Avvenuti, M., Cimino, M.G.C.A., Cresci, S., Marchetti, A., Tesconi, M.: A framework for detecting unfolding emergencies using humans as sensors. Springerplus. 5, 1–23 (2016).
10. Hevner, A.R., March, S.T., Park, J.: Design Science in Information Systems Research. MISQ. 28, 75–106 (2004).
11. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. J. Manag. Inf. Syst. 24, 45–77 (2007).
12. Huang, Q., Xiao, Y.: Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery. ISPRS Int. J. Geo- Information. 4, 1549–1568 (2015).
13. Ahmed, A.: Use of Social Media in Disaster Management. In: Proceedings of the 32nd International Conference on Information Systems (ICIS). , Shanghai, China (2011).
14. MacEachren, A.M., Jaiswal, A., Robinson, A.C., Pezanowski, S., Savelyev, A., Mitra, P., Zhang, X., Blanford, J.: SensePlace2: GeoTwitter Analytics Support for Situational Awareness. In: IEEE Conference on VAST. pp. 181–190., Rhode Island, USA (2011).
15. Alshareef, H.N., Grigoras, D.: Using Social Media and the Mobile Cloud to Enhance Emergency and Risk Management. In: 15th ISPDC. pp. 92–99. IEEE, China (2016).
16. Ashktorab, Z., Brown, C., Nandi, M., Culotta, A.: Tweedr: Mining Twitter to Inform Disaster Response. In: 11th ISCRAM. pp. 354–358. , Pennsylvania, USA (2014).
17. Cobb, C., McCarthy, T., Perkins, A., Bharadwaj, A., Comis, J., Do, B., Starbird, K.: Designing for the Deluge: Understanding & Supporting the Distributed, Collaborative Work of Crisis Volunteers. In: 17th CSCW (2014). pp. 888–899. ACM, Baltimore, USA.
18. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing Social Media Messages in Mass Emergency: A Survey. ACM Comput. Surv. 47, 67:1-67:38 (2015).
19. Stieglitz, S., Mirbabaie, M., Fromm, J., Melzer, S.: The Adoption of social media analytics for crisis management – Challenges and Opportunities. In: ECIS. , Portsmouth (2018).
20. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: A Framework for Evaluation in Design Science Research. Eur. J. Inf. Syst. 25, 77–89 (2016).
21. Grots, A., Pratschke, M.: Design Thinking — Kreativität als Methode. Mark. Rev. St. Gall. 26, 18–23 (2009).
22. Robinson, B., Power, R., Cameron, M.: A Sensitive Twitter Earthquake Detector. In: Proceedings of the 22nd International Conference on World Wide Web – WWW ’13 Companion. pp. 999–1002. ACM, Rio de Janeiro, Brazil (2013).
23. Manaskasemsak, B., Chinthanet, B., Rungsawang, A.: Graph Clustering-Based Emerging Event Detection from Twitter Data Stream. In: 5th International Conference on Network, Communication and Computing – ICNCC ’16. pp. 37–41. ACM, Kyoto, Japan (2016).
24. McCreadie, R., Macdonald, C., Ounis, I.: EAIMS: Emergency Analysis Identification and Management System. In: 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1101–1104. ACM, Pisa, Italy (2016).
25. Cameron, M.A., Power, R., Robinson, B., Yin, J.: Emergency Situation Awareness from Twitter for Crisis Management. In: Proceedings of the 21st International Conference on World Wide Web – WWW ’12 Companion. pp. 695–698. ACM, Lyon, France (2012).
26. Yin, J., Karimi, S., Robinson, B., Cameron, M.: ESA: Emergency Situation Awareness via Microbloggers. In: 21st ACM CIKM ’12. ACM, Maui, Hawaii (2012).
27. Imran, M., Elbassuoni, S., Castillo, C., Diaz, F., Meier, P.: Extracting Information Nuggets from Disaster-Related Messages in Social Media. In: 10th ISCRAM. pp. 791–800. , Baden-Baden, Germany (2013).
28. Pohl, D., Bouchachia, A., Hellwagner, H.: Automatic Identification of Crisis-Related Sub-Events using Clustering. In: 11th ICMLA 2012. pp. 333–338. IEEE, Boca Raton, USA.
29. Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., Tesconi, M.: EARS (Earthquake Alert and Report System): a Real Time Decision Support System for Earthquake Crisis Management. In: 20th ACM SIGKDD ’14. pp. 1749–1758. ACM, New York, USA (2014).
30. Rother, Karl & Nestler: Ein Konzept für die Klassifizierung subjektiver Sicherheit in Tweets, In: B. Weyers, A. Dittmar (Hrsg.): Mensch und Computer Workshopbeiträge, Aachen, 2016
31. UNISDR. (2009). UNISDR Terminology on Disaster Risk Reduction.
32. Yang, J. A., Tsou, M. H., Jung, C. T., Allen, C., Spitzberg, B. H., Gawron, J. M., & Han, S. Y. (2016). Social media analytics and research testbed (SMART): Exploring spatiotemporal patterns of human dynamics with geo-targeted social media messages. Big Data & Society, 3(1), 2053951716652914.
33. Denecke, K., Krieck, M., Otrusina, L., Smrz, P., Dolog, P., Nejdl, W., & Velasco, E. (2013). How to exploit twitter for public health monitoring. Methods of information in medicine, 52(04), 326-339).
Als PDF herunterladen