Abstract
Recent advances in social media have unveiled their potential of providing real-time solutions for disaster management. The work proposed in this paper utilizes Twitter posts to improve flow of information during crisis situations in order to provide support and save lives. The proposed system employs machine learning techniques to perform multiclass classification and filtering important tweets with high degree of accuracy. The proposed system accurately flag tweets about injured or dead people, which we hope can expedite search and rescue efforts of concerned teams. Analysis of the results obtained indicates that efficiency of the system can be further enhanced by using appropriate deep learning techniques
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
G.P. Cooper, V. Yeager, F.M. Burkle, I. Subbarao, Twitter as a potential disaster risk reduction tool. Part I: Introduction, terminology, research and operational applications. PLOS Currents Disasters, Edition 1 (2015)
Twitter usage Statistics. Available at https://www.internetlivestats.com/twitter-statistics/#sources. Accessed on 16 Oct 2020
A. Sinha, P. Kumar, N.P. Rana, R. Islam, Y.K. Dwivedi, Impact of internet of things (IoT) in disaster management: a task-technology fit perspective. Ann. Oper. Res. 283(1), 759–794 (2019)
A. Amirkhanyan, C. Meinel, Analysis of the value of public geotagged data from twitter from the perspective of providing situational awareness, in Social Media: The Good, the Bad, and the Ugly. I3E 2016. Lecture Notes in Computer Science, ed. by Dwivedi Y. et al., vol. 9844 (Springer, Cham, 2016)
S. González-Carvajal, E.C. Garrido-Merchán, Comparing BERT against traditional machine learning text classification. arXiv preprint arXiv:2005.13012. Accessed on 16 Oct 2020 (2020)
L.S. Snyder, M. Karimzadeh, C. Stober, D.S. Ebert, Situational awareness enhanced through social media analytics: a survey of first responders, in 2019 IEEE International Symposium on Technologies for Homeland Security (HST), Woburn, MA, USA (2019), pp. 1–8. https://doi.org/10.1109/HST47167.2019.9033003
X. Wang, F. Zhu, J. Jiang, S. Li, Real time event detection in twitter, in International Conference on Web-Age Information Management, June 2013 (Springer, Berlin, 2013), pp. 502–513
T. Cheng, T. Wicks, Event detection using twitter: a spatio-temporal approach. PLoS ONE 9, e97807 (2014)
X. Zhou, L. Chen, Event detection over twitter social media streams. VLDB J. 23(3), 381–400 (2014)
B. Sriram, D. Fuhry, E. Demir, H. Ferhatosmanoglu, M. Demirbas, Short text classification in twitter to improve information filtering, in Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2010, pp. 841–842
J.P. Singh, Y.K. Dwivedi, N.P. Rana, A. Kumar, K.K. Kapoor, Event classification and location prediction from tweets during disasters. Ann. Oper. Res. 283(1), 737–757 (2019)
A. Joshi, R. Sparks, J. McHugh, S. Karimi, C. Paris, C.R. MacIntyre, Harnessing tweets for early detection of an acute disease event. Epidemiology 31(1), 90 (2020)
M.A. Sit, C. Koylu, I. Demir, Identifying disaster-related tweets and their semantic, spatial and temporal context using deep learning, natural language processing and spatial analysis: a case study of Hurricane Irma. Int. J. Digital Earth 12(11), 1205–1229 (2019)
India Floods 2014. Crisis NLP. Available at: https://crisisnlp.qcri.org/lrec2016/content/2014_india_floods_en.html. Accessed on 16 Oct 2020
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Omar, H., Sinha, A., Kumar, P. (2022). System for Situational Awareness Using Geospatial Twitter Data. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_59
Download citation
DOI: https://doi.org/10.1007/978-981-16-3071-2_59
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3070-5
Online ISBN: 978-981-16-3071-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)