A Literature Review on Big Data and Social Media Usage in Disaster Management

Yıl 2018, Cilt: 1 Sayı: 2, 114 - 125, 31.10.2018

Türkay Dereli , Nazmiye Çelik Cihan Çetinkaya

https://doi.org/10.35341/afet.453396

Cited By: 5

Öz

Most of the disaster management activities are naturally related to traditional operation research and management science applications.  But recently, big data information technology and social media in particular has become an integral part of disaster management. Relevant information taken from social media and the intelligent web has increased the situational awareness of decision makers. Disaster management decisions have important impacts on; the safety of disaster victims, environment, economic systems, organizations etc. Reliable, timely, consistent, sufficient and qualified information is critical in the phases of disaster management. In this study, a literature review is conducted considering big data and social media in the light of disaster management and specifically disaster relief.

Anahtar Kelimeler

Big Data, Disaster Management, Disaster Relief, Literature Review, Social Media

Kaynakça

• Acerbo, F., & Rossi, C. (2017). Filtering Informative Tweets during Emergencies:A Machine Learning Approach. Proceedings of the First CoNEXT Workshop on ICT Tools for Emergency Networks and DisastEr Relief, (pp. 1-6). Incheon, Republic of Korea.
• Alshareef, H., & Grigoras, D. (2016). Using social media and the mobile cloud to enhance emergency and risk management. . 15 th International Symposium on Parallel and Distributed Computing, (pp. 92-99). Fuzhou, China.
• Ancheta, J., Sy, C., Maceda, L., Oco, N., & Roxas, R. (2017). Computer-assisted thematic analysis of Typhoon. Proc. of the 2017 IEEE Region 10 Conference (TENCON), (pp. 723-726). Malaysia.
• Basu, M., Ghosh, K., Das, S., Dey, R., Bandyopadhyay, S., & Ghosh, S. (2017). Identifying Post-Disaster Resource Needs and Availabilities from Microblogs. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (pp. 427-430). Sydney, Australia.
• Caragea, C., McNeese, N., Jaiswal, A., Tarylor, G., & Mitra, P. (2011). Classifying Text Messages for the Haiti Earthquake. 8th International ISCRAM Conference, (pp. 1-10). Lisbon,Portugal.
• Cherichi, S., & Larodec, R. (2016). Using Big Data Values to Enhance Social Event Detection Pattern. Computer Systems and Applications (AICCSA), 2016 IEEE/ACS 13th International Conference of. Morocco.
• Galindo, G., & Batta, R. (2013). Review of recent developments in OR/MS research in disaster operations management. European Journal of Operational Research, 201-211.
• Gupta, S., Altay, N., & Luo, Z. (2017). Big Data in Humanitarian Supply Chain Management: a Review and Further Research Directions. Ann Oper Res.
• IFRC. (2018, May). Retrieved from International Federation of Red Cross and Red Crescent Societies: http://www.ifrc.org/en/what-we-do/disaster-management/about-disasters/what-is-a-disaster/
• Lai, C. (2016). A study of emergent organizing and technological affordances after a natural disaster. Organizing and technological affordances, 507-523.
• Landwehr, P., Wei, W., Kovalchuck, M., & Carley, K. (2016). Using Tweets to Support Disaster Planning, Warning and Response. Safety Science, 33-47.
• Laylavi, F., & Rajabifard, A. K. (2017). Event relatedness assessment of Twitter messages for emergency response. Information Processing and Management, 266-280.
• Li, X., Wang, Z., Gao, C., & Shi, L. (2017). Reasoning human emotional responses from large-scale social and public media. Applied Mathematics and Computation, 182-193.
• Monaghan, A., & Lycett, M. (2013). Big Data and Humanitarian Supply Networks: Can Big Data ive Voice to the Voiceless?, (pp. 432-437). Mulder, F., Ferguson, J., Groenewegen, P., Boersma, K., & Wolbers, J. (2016). Questioning Big Data: Crowdsourcing crisis data towards an inclusive humanitarian response. Big Data & Society, 1-13.
• OATD. (2018). Retrieved from https://oatd.org/
• Onorati, T., Diaz, P., & Carrion, B. (2018). From social networks to emergency operation centers: A semantic visualization approach. Future Generation Computer Systems.
• Papadopulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso-Wamba, S. (2016). The Role of Big Data in Explaining Disaster Resillience in Supply Chains for Sustainability. Journal of Clnear Production, 1-11.
• Soni, R., & Pal, S. (2017). Microblog Retrieval for Disaster Relief: How To Create Ground Truths? Proceedings of the First International Workshop on Exploitation of Social Media for Emergency Relief and Preparedness , (pp. 42-51). Aberdeen, UK.
• Spielhofer, T., Markham, D., Greenlaw, R., & Hahne, A. (2017). Data mining Twitter during the UK floods: Investigating the potential use of social media in emergency management, 2016 3rd International Conference on. Information and Communication Technologies for Disaster Management (ICT-DM). Vienna, Austria: IEEE.
• Su, X. (2018, May). Introduction to Big Data. Retrieved from https://www.ntnu.no/iie/fag/big/lessons/lesson2.pdf
• Wang, Z., & Ye, X. (2018). Social media analytics for natural disaster management. . International Journal of Geographical Information Science, 49-72.
• Wukich, C., Siciliano, M., Enia, J., & Boylan, B. (2016). The formation of transnational knowledge networks on social media. International Public Management Journal, 381–408.
• Wukich, C; Khemka, A. (2017). Social media adoption, message content, and reach:an examination of Red Cross and Red Crescent national societies. Int. J. Emergency Management, 89-116.
• Zou, L., Lam, N., Cai, H., & Qiang, Y. (2018). Mining Twitter Data for Improved Understanding of Disaster Resilience. Annals of the American Association of Geographers, 1-20.