Sosial Media Analisis Berbasis NLP Untuk Mempercepat Tanggap Bencana Banjir

  • Finki Dona Marleny Universitas Muhammadiyah Banjarmasin
  • Mambang Universitas Sari Mulia
Keywords: Social media, Flood, Analysis, NLP, Instagram


Flood disasters that are monitored in real-time on social media can be seen to report directly the condition of the affected areas. Areas that have been warned to be affected by the floods were informed via social media. Surrounding areas that are likely to be affected can be more vigilant by directly speeding up information and getting responses from social media users who are around flood-prone areas. The research aims to provide visualization models that can accelerate flood response information from disaster management, track disasters with increasing vigilance, and accelerate flood disaster recovery with analysis on social media. The approach used with Natural Language Processing (NLP), a data source derived from posts on Instagram is taken for analytical materials. Data sources from Instagram with flood hashtags in Kalimantan are used by using the Natural Language Processing (NLP) process stage to get core information visualizations to speed up flood response information. Visualization of social media data information used based on extracting information from Instagram posts, responses, and hashtags to speed up information provides troubleshooting and the importance of speeding up flood response information. The results of data visualization can accelerate disaster response information to increase awareness of the condition of the surrounding area that can be affected by floods, it can be seen that the amount of data on the hashtag provides data visualization information in accelerating flood disaster management from disaster tracking to disaster recovery.


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D. Wu and Y. Cui, “Disaster early warning and damage assessment analysis using social media data and geo-location information,” Decis. Support Syst., vol. 111, no. April, pp. 48–59, 2018, doi: 10.1016/j.dss.2018.04.005.

C. Fan, C. Zhang, A. Yahja, and A. Mostafavi, “Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management,” Int. J. Inf. Manage., vol. 56, no. November, pp. 1–10, 2021, doi: 10.1016/j.ijinfomgt.2019.102049.

T. Schempp, H. Zhang, A. Schmidt, M. Hong, and R. Akerkar, “A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization,” Int. J. Disaster Risk Reduct., vol. 39, no. August 2018, pp. 2–10, 2019, doi: 10.1016/j.ijdrr.2019.101143.

M. Fernandez, “Risk perceptions and management strategies in a post-disaster landscape of Guatemala: Social conflict as an opportunity to understand disaster,” Int. J. Disaster Risk Reduct., vol. 57, pp. 1–9, 2021, doi: 10.1016/j.ijdrr.2021.102153.

B. E. O. Monte, J. A. Goldenfum, G. P. Michel, and J. R. de A. Cavalcanti, “Terminology of natural hazards and disasters: A review and the case of Brazil,” Int. J. Disaster Risk Reduct., vol. 52, pp. 1–48, 2021, doi: 10.1016/j.ijdrr.2020.101970.

P. Yodsuban and K. Nuntaboot, “Community-based flood disaster management for older adults in southern of Thailand: A qualitative study,” Int. J. Nurs. Sci., vol. 21, pp. 1–31, 2021, doi: 10.1016/j.ijnss.2021.08.008.

J. Fang, J. Hu, X. Shi, and L. Zhao, “Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm,” Int. J. Disaster Risk Reduct., vol. 34, pp. 275–282, 2019, doi: 10.1016/j.ijdrr.2018.11.027.

L. Tan and D. M. Schultz, “Damage classification and recovery analysis of the Chongqing, China, floods of August 2020 based on social-media data,” J. Clean. Prod., vol. 313, no. February, pp. 2–12, 2021, doi: 10.1016/j.jclepro.2021.127882.

S. Chair, M. Charrad, and N. B. Ben Saoud, “Towards A Social Media-Based Framework for Disaster Communication,” Procedia Comput. Sci., vol. 164, pp. 271–278, 2019, doi: 10.1016/j.procs.2019.12.183.

M. K. Htein, S. Lim, and T. N. Zaw, “The evolution of collaborative networks towards more polycentric disaster responses between the 2015 and 2016 Myanmar floods,” Int. J. Disaster Risk Reduct., vol. 31, pp. 964–982, 2018, doi: 10.1016/j.ijdrr.2018.08.003.

I. A. Rana, M. Asim, A. B. Aslam, and A. Jamshed, “Disaster management cycle and its application for flood risk reduction in urban areas of Pakistan,” Urban Clim., vol. 38, no. February, pp. 1–12, Jul. 2021, doi: 10.1016/j.uclim.2021.100893.

Y. Chen, J. Li, and A. Chen, “Does high risk mean high loss: Evidence from flood disaster in southern China,” Sci. Total Environ., vol. 785, no. 15, pp. 1–9, 2021, doi: 10.1016/j.scitotenv.2021.147127.

A. Y. Karunarathne, “Geographies of the evolution of social capital legacies in response to flood disasters in rural and urban areas in Sri Lanka,” Int. J. Disaster Risk Reduct., vol. 62, pp. 1–42, 2021, doi: 10.1016/j.ijdrr.2021.102359.

X. Guan, Y. Zang, Y. Meng, Y. Liu, H. Lv, and D. Yan, “Study on spatiotemporal distribution characteristics of flood and drought disaster impacts on agriculture in China,” Int. J. Disaster Risk Reduct., vol. 64, no. August, pp. 1–13, 2021, doi: 10.1016/j.ijdrr.2021.102504.

K. Uddin and M. A. Matin, “Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology,” Prog. Disaster Sci., vol. 11, no. March 2019, pp. 1–13, 2021, doi: 10.1016/j.pdisas.2021.100185.

M. Dou, Y. Wang, Y. Gu, S. Dong, M. Qiao, and Y. Deng, “Disaster damage assessment based on fine-grained topics in social media,” Comput. Geosci., vol. 156, no. May 2020, pp. 1–12, Nov. 2021, doi: 10.1016/j.cageo.2021.104893.

Z. Xing et al., “Crowdsourced social media and mobile phone signaling data for disaster impact assessment: A case study of the 8.8 Jiuzhaigou earthquake,” Int. J. Disaster Risk Reduct., vol. 58, no. March, pp. 1–12, May 2021, doi: 10.1016/j.ijdrr.2021.102200.

Q. Huang, G. Cervone, and G. Zhang, “A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data,” Comput. Environ. Urban Syst., vol. 66, pp. 23–37, 2017, doi: 10.1016/j.compenvurbsys.2017.06.004.

S. Ozcan, M. Suloglu, C. O. Sakar, and S. Chatufale, “Social media mining for ideation: Identification of sustainable solutions and opinions,” Technovation, vol. 107, pp. 1–12, Sep. 2021, doi: 10.1016/j.technovation.2021.102322.

J. R. Ragini, P. M. R. Anand, and V. Bhaskar, “Big data analytics for disaster response and recovery through sentiment analysis,” Int. J. Inf. Manage., vol. 42, no. May, pp. 15–24, 2018, doi: 10.1016/j.ijinfomgt.2018.05.004.

J. Kim and M. Hastak, “Social network analysis: Characteristics of online social networks after a disaster,” Int. J. Inf. Manage., vol. 38, no. 1, pp. 86–96, Feb. 2018, doi: 10.1016/j.ijinfomgt.2017.08.003.

S. D. Mohanty et al., “A multi-modal approach towards mining social media data during natural disasters - A case study of Hurricane Irma,” Int. J. Disaster Risk Reduct., vol. 54, no. January, pp. 1–14, Feb. 2021, doi: 10.1016/j.ijdrr.2020.102032.

Y. Yan, J. Chen, and Z. Wang, “Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations,” Appl. Geogr., vol. 123, pp. 1–13, Oct. 2020, doi: 10.1016/j.apgeog.2020.102306.

F. Yuan, M. Li, R. Liu, W. Zhai, and B. Qi, “Social media for enhanced understanding of disaster resilience during Hurricane Florence,” Int. J. Inf. Manage., vol. 57, no. November 2020, pp. 1–18, Apr. 2021, doi: 10.1016/j.ijinfomgt.2020.102289.

M. Gridach, “A framework based on (probabilistic) soft logic and neural network for NLP,” Appl. Soft Comput. J., vol. 93, pp. 1–8, 2020, doi: 10.1016/j.asoc.2020.106232.

D. Sellers, J. Crilly, and J. Ranse, “Disaster preparedness: A concept analysis and its application to the intensive care unit,” Aust. Crit. Care, pp. 1–6, 2021, doi: 10.1016/j.aucc.2021.04.005.

N. Jung and G. Lee, “Automated classification of building information modeling (BIM) case studies by BIM use based on natural language processing (NLP) and unsupervised learning,” Adv. Eng. Informatics, vol. 41, no. March, pp. 1–10, 2019, doi: 10.1016/j.aei.2019.04.007.

A. W. Olthof et al., “Machine learning based natural language processing of radiology reports in orthopaedic trauma,” Comput. Methods Programs Biomed., vol. 208, pp. 1–9, 2021, doi: 10.1016/j.cmpb.2021.106304.

M. Zhou, N. Duan, S. Liu, and H. Y. Shum, “Progress in Neural NLP: Modeling, Learning, and Reasoning,” Engineering, vol. 6, no. 3, pp. 275–290, 2020, doi: 10.1016/j.eng.2019.12.014.

B. Yang, L. Wang, D. F. Wong, S. Shi, and Z. Tu, “Context-aware Self-Attention Networks for Natural Language Processing,” Neurocomputing, vol. 458, pp. 157–169, 2021, doi: 10.1016/j.neucom.2021.06.009.

G. Perboli, M. Gajetti, S. Fedorov, and S. Lo Giudice, “Natural Language Processing for the identification of Human factors in aviation accidents causes: An application to the SHEL methodology,” Expert Syst. Appl., vol. 186, no. June, pp. 1–7, 2021, doi: 10.1016/j.eswa.2021.115694.

C. J. De Torre, S. D, B. I, and M.-B. M.J, “Text Mining : Techniques , Applications , and Challenges,” Int. J. Uncertain., vol. 26, no. 4, pp. 553–582, 2018, doi: 10.1142/S0218488518500265.

M. Staniewski and K. Awruk, “Technological Forecasting & Social Change The influence of Instagram on mental well-being and purchasing decisions in a pandemic,” Technol. Forecast. Soc. Chang., vol. 174, p. 121287, 2022, doi: 10.1016/j.techfore.2021.121287.

J. Kim and M. Hastak, “Social network analysis: Characteristics of online social networks after a disaster,” Int. J. Inf. Manage., vol. 38, no. 1, 2018, doi: 10.1016/j.ijinfomgt.2017.08.003.

How to Cite
Marleny, F. D., & Mambang. (2022). Sosial Media Analisis Berbasis NLP Untuk Mempercepat Tanggap Bencana Banjir . Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal), 9(1), 1-7.