Taxonomy of Community Response During Flood Disasters in Jakarta: A Communication Perspective

https://doi.org/10.5614/sostek.itbj.2024.23.3.10

Authors

  • Martha Lestari Telkom University
  • Anisa Diniati Digital Public Relations Department, Telkom University, Bandung, Indonesia
  • Purnama Alamsyah Pusat Riset Kesejahteraan Sosial, Desa, dan Konektivitas, Badan Riset dan Inovasi Nasional, Jakarta, Indonesia & Magister Student, Sekolah Arsitektur, Perencanaan, dan Pengembangan Kebijakan, Institut Teknologi Bandung, Bandung, Indonesia
  • Aqida Nuril Salma Digital Public Relations Department, Telkom University, Bandung, Indonesia

Keywords:

flooding, public response, topic model, Twitter

Abstract

Jakarta has experienced numerous significant floods since 2002, including those in 2007, 2013, and 2015, a source of concern for the residents of Jakarta. Residents of DKI Jakarta have expressed their concerns regarding the disaster on Twitter and other platforms, which offer a comprehensive account of the flood experiences of Jakarta residents, which is beneficial to stakeholders in the field of flood management. The status of an incident can be rapidly determined using Twitter data. This project investigates the Twitter responses of Jakartans to the flood disaster. The analysis of flood tweets from Jakarta residents is conducted in three phases: 2015, 2020, and 2021. Utilizing topic modeling, this initiative maps emerging topics. Topic modeling is particularly effective in the context of flood issues, as it can assist in the clustering of topics and the
dissemination of specific information to the public. Seven modeling-based topic groups are used to organize twenty topics. The results of this project indicate that the majority of Jakartan flood tweets are related to "Weather Report" topics. The findings indicated that "Submerged Houses" has a substantial proportion and is anticipated to increase with each flood. The increasing number of submerged houses in Jakarta should cause concern among the local populace.

References

Dynes, R. R. (1994). Community emergency planning: false assumptions and inappropriate

analogies. International Journal of Mass Emergencies & Disasters, 12(2), 141–158. https://doi.

org/10.1177/028072709401200201

Firman, T., Surbakti, I. M., Idroes, I. C., & Simarmata, H. A. (2011). Potential climate-change related

vulnerabilities in Jakarta: Challenges and current status. Habitat International, 35(2), 372–378.

https://doi.org/10.1016/j.habitatint.2010.11.011

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2014). The rise of “big

data” on cloud computing: Review and open research issues. Information Systems, 47, 98–115.

https://doi.org/10.1016/j.is.2014.07.006

Instagram. (n.d.). https://www.instagram.com/p/CLmUnpMgHjZ/

JustAnotherArchivist. (n.d.). GitHub - JustAnotherArchivist/snscrape: A social networking service

scraper in Python. GitHub. https://github.com/JustAnotherArchivist/snscrape

Karami, A., Shah, V., Vaezi, R., & Bansal, A. (2019). Twitter speaks: A case of national disaster

situational awareness. Journal of Information Science, 46(3), 313–324. https://doi.

org/10.1177/0165551519828620

Kersten, J., & Klan, F. (2020). What happens during disasters? A Workflow for the multifaceted

characterization of crisis events based on Twitter data. Journal of Contingencies and Crisis

Management, 28(3), 262–280. https://doi.org/10.1111/1468-5973.12321

Knezic, S., Baucic, M., Kekez, T., Delprato, U., Tusa, G., Preinersdorfer, A., & Lichtenegger, G. C.

(2015). Taxonomy for disaster response: a methodological approach. Conference: 22nd Anual

Conference of the International Emergency Management Society TIEMS.

Middleton, S. E., Middleton, L., & Modafferi, S. (2014). Real-Time crisis mapping of natural disasters

using social media. IEEE Intelligent Systems, 29(2), 9–17. https://doi.org/10.1109/mis.2013.126

Nguyen, D. T., & Jung, J. E. (2016). Real-time event detection for online behavioral analysis of big

social data. Future Generation Computer Systems, 66, 137–145. https://doi.org/10.1016/j.

future.2016.04.012

Overview. (n.d.). Docs | Twitter Developer Platform. https://developer.twitter.com/en/docs/twitter-api/

v1/tweets/search/overview

Pantau Banjir Jakarta. (n.d.). Pantaubanjir.jakarta.go.id. https://pantaubanjir.jakarta.go.id/

Resch, B., Usländer, F., & Havas, C. (2017). Combining machine-learning topic models and spatiotemporal

analysis of social media data for disaster footprint and damage assessment. Cartography and

Geographic Information Science, 45(4), 362–376. https://doi.org/10.1080/15230406.2017.1356242

Roche, S., Propeck-Zimmermann, E., & Mericskay, B. (2011). GeoWeb and crisis management: issues

and perspectives of volunteered geographic information. GeoJournal, 78(1), 21–40. https://doi.

org/10.1007/s10708-011-9423-9

Villars, R., Olofson, C., & Eastwood, M. (2011). White Paper Big Data: What It is and Why You Should

Care. https://www.admin-magazine.com/HPC/content/download/5604/49345/file/IDC_ BigData_

whitepaper_final.pdf

Wijayanti, P., Zhu, X., Hellegers, P., Budiyono, Y., & Van Ierland, E. C. (2016). Estimation of river

flood damages in Jakarta, Indonesia. Natural Hazards, 86(3), 1059–1079. https://doi.org/10.1007/

s11069-016-2730-1

Yu, M., Yang, C., & Li, Y. (2018). Big data in natural disaster management: A review. Geosciences, 8(5),

https://doi.org/10.3390/geosciences8050165

Published

2024-11-21