Typhoon Risk Perception: A Case Study of Typhoon Lekima in China

Abstract

The typhoon is one major threat to human societies and natural ecosystems, and its risk perception is crucial for contextualizing and managing disaster risks in different social settings. Social media data are a new data source for studying risk perception, because such data are timely, widely distributed, and sensitive to emergencies. However, few studies have focused on crowd sensitivity variation in social media data-based typhoon risk perception. Based on the regional disaster system theory, a framework of analysis for crowd risk perception was established to explore the feasibility of using social media data for typhoon risk perception analysis and crowd sensitivity variation. The goal was to quantitatively analyze the impact of hazard intensity and social and geographical environments on risk perception and its variation among population groups. Taking the Sina Weibo data during Typhoon Lekima of 2019 as an example, we found that: (1) Typhoon Lekima-related Weibo public attention changed in accordance with the evolution of the typhoon track and the number of Weibo posts shows a significantly positive correlation with disaster losses, while socioeconomic factors, including population, gross domestic product, and land area, are not explanatory factors of the spatial distribution of disaster-related Weibo posts; (2) Females, nonlocals with travel plans, and people living in areas with high hazard intensity, low elevation, or near waterbodies affected by Lekima paid more attention to the typhoon disaster; and (3) Descriptions of rainfall intensity by females are closer to the meteorological observation data.

Keywords

China / Crowd sensitivity / Regional disaster system theory / Risk perception / Social media / Typhoon Lekima

References

[1] Aak, A., A. Iar, and B. An. 2020. Gender-based approach for assessing risk perception in a multi-hazard environment: A study of high schools of Gilgit, Pakistan. International Journal of Disaster Risk Reduction 44: Article 101427.

[2] Albrecht, R., J.B. Jarecki, D.S. Meier, and J. Rieskamp. 2021. Risk preferences and risk perception affect the acceptance of digital contact tracing. Humanities and Social Sciences Communications 8: Article 195.

[3] Bao, X., L. Wu, S. Zhang, H. Yuan, and H. Wang. 2020. A comparison of convective raindrop size distributions in the eyewall and spiral rainbands of Typhoon Lekima (2019). Geophysical Research Letters 47(23): Article 2020GL090729.

[4] Baranowski, D.B., M.K. Flatau, P.J. Flatau, D. Karnawati, K. Barabasz, M. Labuz, B. Latos, J.M. Schmidt, et al. 2020. Social-media and newspaper reports reveal large-scale meteorological drivers of floods on Sumatra. Nature Communications 11: Article 2503.

[5] Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022.

[6] Butler, D. 2013. Crowdsourcing goes mainstream in typhoon response. Nature 2013(14186): 2022. https://doi.org/10.1038/nature.2013.14186. Accessed 20 Feb 2022.

[7] Cai W, Liu B, Wei Z, Li M, Kan J. TARDB-Net: Triple-attention guided residual dense and BiLSTM networks for hyperspectral image classification. Multimedia Tools and Applications, 2021, 80(7): 11291-11312

[8] de Diego IM, Fernandez-Isabel A, Ortega F, Moguerza JM. A visual framework for dynamic emotional web analysis. Knowledge-Based Systems, 2018, 145: 264-273

[9] Eyre, R., F.D. Luca, and F. Simini. 2020. Social media usage reveals recovery of small businesses after natural hazard events. Nature Communications 11: Article 1629.

[10] Fan C, Wu F, Mostafavi A. A hybrid machine learning pipeline for automated mapping of events and locations from social media in disasters. IEEE Access, 2020, 8(8): 10478-10490

[11] Ginige K, Amaratunga DG, Haigh R. Mainstreaming gender in disaster reduction: Why and how?. Disaster Prevention and Management, 2009, 18(1): 23-34

[12] Gotham KF, Campanella R, Lauve-Moon K, Powers B. Hazard experience, geophysical vulnerability, and flood risk perceptions in a postdisaster city, the case of New Orleans. Risk Analysis, 2018, 38(2): 345-356

[13] Harman, J.L., J.M. Weinhardt, J.W. Beck, and I. Mai. 2021. Interpreting time-series COVID data: Reasoning biases, risk perception, and support for public health measures. Scientific Reports 11(1): Article 15585.

[14] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780

[15] Hu Y, Gao S, Lunga D, Li W, Newsam S, Bhaduri B. GeoAI at ACM SIGSPATIAL: Progress, challenges, and future directions. ACM Sigspatial Special, 2019, 11(2): 5-15

[16] Ilieva RT, Mcphearson T. Social-media data for urban sustainability. Nature Sustainability, 2018, 1(10): 553-565

[17] Jelodar H, Wang Y, Chi Y, Xia F, Jiang X, Li Y, Zhao L. Latent Dirichlet Allocation (LDA) and topic modeling: Models, applications, a survey. Multimedia Tools and Applications, 2018, 78(11): 15169-15211

[18] Jia JY, Han LY, Liu YF, He N, Zhang Q, Wan X, Zhang YF, Hu JM. Drought risk analysis of maize under climate change based on natural disaster system theory in Southwest China. Acta Ecologica Sinica, 2016, 36(5): 340-349

[19] Khare A, He Q, Batta R. Predicting gasoline shortage during disasters using social media. OR Spectrum, 2020, 42(3): 693-726

[20] Kryvasheyeu, Y., H. Chen, N. Obradovich, E. Moro, and P.V. Hentenryck. 2016. Rapid assessment of disaster damage using social media activity. Science Advances 2(3): Article e1500779.

[21] Lee TM, Markowitz EM, Howe PD, Ko C-Y, Leiserowitz AA. Predictors of public climate change awareness and risk perception around the world. Nature Climate Change, 2015, 5: 1014-1020

[22] Li Z, Li Q, Zou X, Ren J. Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings. ScienceDirect, 2021, 423: 207-219.

[23] Ma Y, Yu D, Wu T, Wang H. PaddlePaddle: An open-source deep learning platform from industrial practice. Frontiers of Data and Computing, 2019, 1(1): 105-115.

[24] Macias T. Environmental risk perception among race and ethnic groups in the United States. Ethnicities, 2016, 16(1): 111-129

[25] Marshall M. Mystery of deadly Indonesian tsunami cracked using social-media videos. Nature, 2019, 569(7757): 463-464

[26] Onan A, Korukoglu S, Bulut H. Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 2016, 57(9): 232-247

[27] Rahmanti, A.R., D.N.A. Ningrum, L. Lazuardi, H.C. Yang, and Y.C.J Li. 2021. Social media data analytics for outbreak risk communication: Public attention on the "new normal" during the COVID-19 pandemic in Indonesia. Computer Methods and Programs in Biomedicine 205: Article 106083.

[28] Sagcan, M., and P. Senkul. 2015. Toponym recognition in social media for estimating the location of events. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 14–17 November 2015, Atlantic City, NJ, USA, 33–39.

[29] Shi, P. 1991. On the theory of disaster research and its practice. Journal of Nanjing University Natural Science 11: 37–42 (in Chinese).

[30] Shi, P. 1996. Theory and practice of disaster study. Journal of Natural Disasters 5(4): 6–17 (in Chinese).

[31] Shi, P. 2002. Theory on disaster science and disaster dynamics. Journal of Natural Disasters 11(3): 1–9 (in Chinese).

[32] Shi, P. 2005. Theory and practice on disaster system research on a fourth time. Journal of Natural Disasters 14(6): 1–7 (in Chinese).

[33] Shi, P. 2009. Theory and practice on disaster system research on a fifth time. Journal of Natural Disasters 18(5): 1–9 (in Chinese).

[34] Siami-Namini, S., N. Tavakoli, and A.S. Namin. 2019. The performance of LSTM and BiLSTM in forecasting time series. In Proceedings of 2019 IEEE International Conference on Big Data, 9–12 December 2019, Los Angeles, CA, USA, 3285–3292.

[35] Tang, J., S. Yang, and W. Wang. 2021. Social media-based disaster research: Development, trends, and obstacles. International Journal of Disaster Risk Reduction 55: Article 102095.

[36] UNDRR (United Nations Office for Disaster Risk Reduction). 2020. 2019 global natural disaster assessment report. https://www.preventionweb.net/files/73363_2019globalnaturaldisasterassessment.pdf. Accessed 10 Jan 2022.

[37] Ushiyama M, Yoshida A, Kashiwagi N, Sato S, Sato Y. Analysis of the effects of disaster workshop for non-residents on the participants. Journal of Japan Society for Natural Disaster Science, 2009, 27(4): 375-385.

[38] Wang, B., B.P.Y. Loo, F. Zhen, and G. Xi. 2020. Urban resilience from the lens of social media data: Responses to urban flooding in Nanjing, China. Cities 106: Article 102884.

[39] Xiao Y, Huang Q, Zhang C. Research on urban disaster analysis based on the big data mining of social media: Case study of Hurricane Sandy in New York. Urban Planning International, 2018, 33(4): 84-92

[40] Yang, P., X. Huang, L. Peng, Z. Zheng, X. Wu, and C. Xing. 2020. Safety evaluation of major hazard installations based on regional disaster system theory. Journal of Loss Prevention in The Process Industries 69: Article 104346.

[41] Yao, K., S. Yang, and J. Tang. 2021. Rapid assessment of seismic intensity based on Sina Weibo—A case study of the changning earthquake in Sichuan Province, China. International Journal of Disaster Risk Reduction 58: Article 102217.

[42] Zhang L, Ai W, Luo X. Research on communication of network information under meteorological disaster: Case of typhoon. Journal of the Meteorological Sciences, 2020, 40(6): 868-874.

[43] Zhao X, Zhan MM, Liu BF. Understanding motivated publics during disasters: Examining message functions, frames, and styles of social media influentials and followers. Journal of Contingencies and Crisis Management, 2019, 27(4): 387-399

[44] Zheng S, Wang J, Sun C, Zhang X, Matthew EK. Air pollution lowers Chinese urbanites’ expressed happiness on social media. Nature Human Behaviour, 2019, 3(3): 237-243