{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T17:04:22Z","timestamp":1784307862512,"version":"3.55.0"},"reference-count":120,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["AI &amp; Soc"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00146-025-02423-6","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T02:37:10Z","timestamp":1750991830000},"page":"505-526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mapping out AI functions in intelligent disaster (mis)management and AI-caused disasters"],"prefix":"10.1007","volume":"41","author":[{"given":"Yasser","family":"Pouresmaeil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saleh","family":"Afroogh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfeng","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"issue":"22","key":"2423_CR1","doi-asserted-by":"publisher","first-page":"12560","DOI":"10.3390\/su132212560","volume":"13","author":"SK Abid","year":"2021","unstructured":"Abid SK et al (2021) Toward an integrated disaster management approach: how artificial intelligence can boost disaster management. Sustainability 13(22):12560. https:\/\/doi.org\/10.3390\/su132212560","journal-title":"Sustainability"},{"key":"2423_CR2","doi-asserted-by":"publisher","unstructured":"Adnan R, Ruslan FA, Samad AM, Zain ZM (2012) Artificial neural network modelling and flood water level prediction using extended Kalman filter. In: 2012 IEEE international conference on control system, computing and engineering, pp 535\u2013538. https:\/\/doi.org\/10.1109\/ICCSCE.2012.6487204.","DOI":"10.1109\/ICCSCE.2012.6487204"},{"key":"2423_CR3","doi-asserted-by":"publisher","unstructured":"Adnan R, Ruslan FA, Samad AM, Zain ZM (2012) Artificial neural network modelling and flood water level prediction using extended Kalman filter. In: 2012 IEEE international conference on control system, computing and engineering, pp 535\u2013538. https:\/\/doi.org\/10.1109\/ICCSCE.2012.6487204","DOI":"10.1109\/ICCSCE.2012.6487204"},{"issue":"2","key":"2423_CR4","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s43681-022-00174-4","volume":"3","author":"S Afroogh","year":"2023","unstructured":"Afroogh S (2023) A probabilistic theory of trust concerning artificial intelligence: can intelligent robots trust humans? AI Ethics 3(2):469\u2013484","journal-title":"AI Ethics"},{"key":"2423_CR5","doi-asserted-by":"publisher","first-page":"100698","DOI":"10.1016\/j.jemep.2021.100698","volume":"18","author":"S Afroogh","year":"2021","unstructured":"Afroogh S, Kazemi A, Seyedkazemi A (2021a) COVID-19, scarce resources and priority ethics: why should maximizers be more conservative? Ethics Med Public Health 18:100698. https:\/\/doi.org\/10.1016\/j.jemep.2021.100698","journal-title":"Ethics Med Public Health"},{"issue":"7","key":"2423_CR6","doi-asserted-by":"publisher","first-page":"4060","DOI":"10.3390\/su13074060","volume":"13","author":"S Afroogh","year":"2021","unstructured":"Afroogh S, Esmalian A, Donaldson JP, Mostafavi A (2021b) Empathic design in engineering education and practice: an approach for achieving inclusive and effective community resilience. Sustainability 13(7):4060","journal-title":"Sustainability"},{"issue":"3","key":"2423_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10676-022-09659-6","volume":"24","author":"S Afroogh","year":"2022","unstructured":"Afroogh S et al (2022) Tracing app technology: an ethical review in the COVID-19 era and directions for post-COVID-19. Ethics Inf Technol 24(3):30. https:\/\/doi.org\/10.1007\/s10676-022-09659-6","journal-title":"Ethics Inf Technol"},{"key":"2423_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-023-00309-1","author":"S Afroogh","year":"2023","unstructured":"Afroogh S et al (2023) Embedded ethics for responsible artificial intelligence systems (EE-RAIS) in disaster management: a conceptual model and its deployment. AI Ethics. https:\/\/doi.org\/10.1007\/s43681-023-00309-1","journal-title":"AI Ethics"},{"issue":"1","key":"2423_CR9","first-page":"1","volume":"11","author":"S Afroogh","year":"2024","unstructured":"Afroogh S, Akbari A, Malone E, Kargar M, Alambeigi H (2024) Trust in AI: progress, challenges, and future directions. Human Soc Sci Commun 11(1):1\u201330","journal-title":"Human Soc Sci Commun"},{"key":"2423_CR10","doi-asserted-by":"crossref","unstructured":"Ahern D (2025) The New Anticipatory Governance Culture for Innovation: Regulatory Foresight, Regulatory Experimentation and Regulatory Learning. arXiv preprint arXiv:2501.05921.","DOI":"10.1007\/s40804-025-00348-7"},{"key":"2423_CR11","unstructured":"AI training for government officials: bridging the knowledge gap. resiliencebuilder.co (2024)"},{"issue":"5","key":"2423_CR12","doi-asserted-by":"publisher","first-page":"999","DOI":"10.5194\/nhess-19-999-2019","volume":"19","author":"S Ali","year":"2019","unstructured":"Ali S, Biermanns P, Haider R, Reicherter K (2019) Landslide susceptibility mapping by using a geographic information system (GIS) along the China-Pakistan Economic Corridor (Karakoram Highway), Pakistan. Nat Hazards Earth Syst Sci 19(5):999\u20131022. https:\/\/doi.org\/10.5194\/nhess-19-999-2019","journal-title":"Nat Hazards Earth Syst Sci"},{"issue":"2","key":"2423_CR13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.3390\/ai1020009","volume":"1","author":"Z Allam","year":"2020","unstructured":"Allam Z, Dey G, Jones DS (2020) Artificial intelligence (AI) provided early detection of the coronavirus (COVID-19) in China and will influence future Urban health policy internationally. AI 1(2):156\u2013165","journal-title":"AI"},{"key":"2423_CR14","doi-asserted-by":"publisher","unstructured":"Anderljung M et al (2023) Frontier AI regulation: managing emerging risks to public safety. https:\/\/doi.org\/10.48550\/arXiv.2307.03718","DOI":"10.48550\/arXiv.2307.03718"},{"key":"2423_CR15","doi-asserted-by":"crossref","unstructured":"Anindita AP, Laksono P, Bagus G, Nugraha B (2016) Dam water level prediction system utilizing artificial neural network back propagation: case study: Ciliwung watershed, Katulampa Dam. In: International conference on ICT For smart society (ICISS). IEEE","DOI":"10.1109\/ICTSS.2016.7792862"},{"key":"2423_CR16","unstructured":"Ashktorab Z (2014) Tweedr: mining twitter to inform disaster response. In: ISCRAM"},{"key":"2423_CR17","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-319-98446-9_19","volume-title":"Lecture notes in computer science","author":"ME Aydin","year":"2018","unstructured":"Aydin ME, Fellows R (2018) Building collaboration in multi-agent systems using reinforcement learning. In: Nguyen NT, Pimenidis E, Khan Z, Trawi\u0144ski B (eds) Lecture notes in computer science. Springer International Publishing, Berlin, pp 201\u2013212. https:\/\/doi.org\/10.1007\/978-3-319-98446-9_19"},{"issue":"2","key":"2423_CR18","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.jnlssr.2021.05.002","volume":"2","author":"R Ba","year":"2021","unstructured":"Ba R, Deng Q, Liu Y, Yang R, Zhang H (2021) Multi-hazard disaster scenario method and emergency management for urban resilience by integrating experiment\u2013simulation\u2013field data. J Saf Sci Resil 2(2):77\u201389. https:\/\/doi.org\/10.1016\/j.jnlssr.2021.05.002","journal-title":"J Saf Sci Resil"},{"key":"2423_CR19","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1016\/j.ins.2009.12.032","volume":"180","author":"B Baruque","year":"2010","unstructured":"Baruque B et al (2010) A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf Sci 180:2029\u20132043","journal-title":"Inf Sci"},{"key":"2423_CR20","doi-asserted-by":"publisher","first-page":"134413","DOI":"10.1016\/j.scitotenv.2019.134413","volume":"701","author":"DT Bui","year":"2020","unstructured":"Bui DT, Hoang ND, Mart\u00ednez-\u00c1lvarez F, Ngo PTT, Hoa PV, Pham TD (2020) A novel deep learning neural network approach for predicting flash flood susceptibility: a case study at a high frequency tropical storm area. Sci Total Environ 701:134413","journal-title":"Sci Total Environ"},{"key":"2423_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.jbusres.2020.10.012","volume":"131","author":"AI Canhoto","year":"2021","unstructured":"Canhoto AI (2021) Leveraging machine learning in the global fight against money laundering and terrorism financing: an affordances perspective. J Bus Res 131:441\u2013452","journal-title":"J Bus Res"},{"key":"2423_CR22","doi-asserted-by":"publisher","first-page":"104581","DOI":"10.1016\/j.ssci.2019.104581","volume":"124","author":"F Cavdur","year":"2020","unstructured":"Cavdur F, Sebatli-Saglam A, Kose-Kucuk M (2020a) A spreadsheet-based decision support tool for temporary-disaster-response facilities allocation. Saf Sci 124:104581. https:\/\/doi.org\/10.1016\/j.ssci.2019.104581","journal-title":"Saf Sci"},{"key":"2423_CR23","doi-asserted-by":"publisher","first-page":"104581","DOI":"10.1016\/j.ssci.2019.104581","volume":"124","author":"F Cavdur","year":"2020","unstructured":"Cavdur F, Sebatli-Saglam A, Kose-Kucuk M (2020b) A spreadsheet-based decision support tool for temporary-disaster-response facilities allocation. Saf Sci 124:104581. https:\/\/doi.org\/10.1016\/j.ssci.2019.104581","journal-title":"Saf Sci"},{"key":"2423_CR24","doi-asserted-by":"publisher","unstructured":"Cen J, Yu T, Li Z, Jin S, Liu S (2011) Developing a disaster surveillance system based on wireless sensor network and cloud platform. In: IET international conference on communication technology and application (ICCTA 2011), 2011, pp 757\u2013761. https:\/\/doi.org\/10.1049\/cp.2011.0770.","DOI":"10.1049\/cp.2011.0770"},{"key":"2423_CR25","unstructured":"Cen J et al (2011) Developing a disaster surveillance system based on wireless sensor network and cloud platform. In: IET international conference on communication technology and application (ICCTA 2011)"},{"issue":"12","key":"2423_CR26","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1080\/01446190600953805","volume":"24","author":"WT Chen","year":"2006","unstructured":"Chen WT, Huang Y-H (2006) Approximately predicting the cost and duration of school reconstruction projects in Taiwan. Constr Manag Econ 24(12):1231\u20131239","journal-title":"Constr Manag Econ"},{"issue":"7","key":"2423_CR27","doi-asserted-by":"publisher","first-page":"676","DOI":"10.3390\/atmos11070676","volume":"11","author":"R Chen","year":"2020","unstructured":"Chen R, Zhang W, Wang X (2020) Machine learning in tropical cyclone forecast modeling: a review. Atmosphere (Basel) 11(7):676","journal-title":"Atmosphere (Basel)"},{"key":"2423_CR28","doi-asserted-by":"publisher","unstructured":"Chen J, Storchan V, Kurshan E (2021) Beyond fairness metrics: roadblocks and challenges for ethical AI in practice. https:\/\/doi.org\/10.48550\/arXiv.2108.06217","DOI":"10.48550\/arXiv.2108.06217"},{"key":"2423_CR29","doi-asserted-by":"publisher","unstructured":"Cherian CM, Jayaraj N, Vaidyanathan SG (2010) Artificially intelligent tsunami early warning system. In: 2010 12th international conference on computer modelling and simulation, pp 39\u201344. https:\/\/doi.org\/10.1109\/UKSIM.2010.16","DOI":"10.1109\/UKSIM.2010.16"},{"key":"2423_CR30","doi-asserted-by":"publisher","unstructured":"Cherian CM, Jayaraj N, Vaidyanathan SG (2010) Artificially intelligent tsunami early warning system. In: 2010 12th international conference on computer modelling and simulation, pp 39\u201344. https:\/\/doi.org\/10.1109\/UKSIM.2010.16.","DOI":"10.1109\/UKSIM.2010.16"},{"key":"2423_CR31","doi-asserted-by":"publisher","first-page":"123929","DOI":"10.1016\/j.jhydrol.2019.123929","volume":"577","author":"B Choubin","year":"2019","unstructured":"Choubin B, Borji M, Mosavi A, Sajedi-Hosseini F, Singh VP, Shamshirband S (2019) Snow avalanche hazard prediction using machine learning methods. J Hydrol (Amst) 577:123929. https:\/\/doi.org\/10.1016\/j.jhydrol.2019.123929","journal-title":"J Hydrol (Amst)"},{"key":"2423_CR32","doi-asserted-by":"crossref","unstructured":"Chowdhury FH et al (2017) Design, control & performance analysis of forecast junction IoT and swarm robotics based system for natural disaster monitoring. In: 8th international conference on computing, communication and networking technologies (ICCCNT). IEEE","DOI":"10.1109\/ICCCNT.2017.8204148"},{"key":"2423_CR33","unstructured":"Dangwal A (2022) Ukraine uses \u2018controversial\u2019 artificial intelligence tech in its war against Russia as Kiev looks to win the \u2018digital war\u2019. The Guardian"},{"key":"2423_CR34","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.compenvurbsys.2012.02.003","volume":"36","author":"E Danso-Amoako","year":"2012","unstructured":"Danso-Amoako E et al (2012) Predicting dam failure risk for sustainable flood retention basins: a generic case study for the wider Greater Manchester area. Comput Environ Urban Syst 36:423\u2013433","journal-title":"Comput Environ Urban Syst"},{"issue":"3","key":"2423_CR36","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/5289.863909","volume":"3","author":"JE DeVault","year":"2000","unstructured":"DeVault JE (2000a) Robotic system for underwater inspection of bridge piers. IEEE Instrum Meas Mag 3(3):32\u201337. https:\/\/doi.org\/10.1109\/5289.863909","journal-title":"IEEE Instrum Meas Mag"},{"issue":"3","key":"2423_CR37","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/5289.863909","volume":"3","author":"JE DeVault","year":"2000","unstructured":"DeVault JE (2000b) Robotic system for underwater inspection of bridge piers. IEEE Instrum Meas Mag 3(3):32\u201337. https:\/\/doi.org\/10.1109\/5289.863909","journal-title":"IEEE Instrum Meas Mag"},{"key":"2423_CR38","unstructured":"Dialani P (2019) Different ways of how twitter uses artificial intelligence. Artificial intelligence latest news. https:\/\/www.analyticsinsight.net\/artificial-intelligence\/different-ways-of-how-twitter-uses-artificial-intelligence."},{"key":"2423_CR39","doi-asserted-by":"publisher","unstructured":"Djordjevich DD, Xavier PG, Bernard ML, Whetzel JH, Glickman MR, Verzi SJ (2008) Preparing for the aftermath: using emotional agents in game-based training for disaster response. In: 2008 IEEE symposium on computational intelligence and games, pp 266\u2013275. https:\/\/doi.org\/10.1109\/CIG.2008.5035649","DOI":"10.1109\/CIG.2008.5035649"},{"issue":"4","key":"2423_CR40","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1080\/13658816.2012.721554","volume":"27","author":"AC Teodoro","year":"2013","unstructured":"Teodoro AC, Duarte L (2013) Forest fire risk maps: a GIS open source application\u2014a case study in Norwest of Portugal. Int J Geogr Inf Sci 27(4):699\u2013720. https:\/\/doi.org\/10.1080\/13658816.2012.721554","journal-title":"Int J Geogr Inf Sci"},{"key":"2423_CR41","unstructured":"Duggan PM (2015) Strategic development of special warfare in cyberspace, p 8. files\/8290\/Duggan-2015-Strategic Development of Special Warfare in Cybers.pdf"},{"issue":"2","key":"2423_CR42","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/s10796-013-9466-1","volume":"17","author":"S Fang","year":"2015","unstructured":"Fang S et al (2015) An integrated information system for snowmelt flood early-warning based on internet of things. Inf Syst Front 17(2):321\u2013335. https:\/\/doi.org\/10.1007\/s10796-013-9466-1","journal-title":"Inf Syst Front"},{"key":"2423_CR43","doi-asserted-by":"crossref","unstructured":"G\u00e4hler M (2016) Remote sensing for natural or man-made disasters and environmental changes. Environmental applications of remote sensing, 309-338.","DOI":"10.5772\/62183"},{"issue":"1","key":"2423_CR44","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s11069-017-2959-3","volume":"89","author":"F Gauthier","year":"2017","unstructured":"Gauthier F, Germain D, H\u00e9tu B (2017) Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gasp\u00e9sie, Qu\u00e9bec, Canada. Nat Hazards 89(1):201\u2013232","journal-title":"Nat Hazards"},{"key":"2423_CR45","doi-asserted-by":"crossref","unstructured":"Gill DA, Ritchie LA (2017) Contributions of technological and natech disaster research to the social science disaster paradigm. In Handbook of disaster research (pp. 39-60). Cham: Springer International Publishing.","DOI":"10.1007\/978-3-319-63254-4_3"},{"key":"2423_CR46","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1080\/17538941003759255","volume":"3","author":"M Goodchild","year":"2010","unstructured":"Goodchild M, Glennon A (2010) Crowdsourcing geographic information for disaster response: a research frontier. Int J Digit Earth 3:231\u2013241","journal-title":"Int J Digit Earth"},{"key":"2423_CR47","volume-title":"Thirty years of natural disasters 1974\u20132003: the numbers","author":"D Guha-Sapir","year":"2004","unstructured":"Guha-Sapir D (2004) Thirty years of natural disasters 1974\u20132003: the numbers. Presses univ. de Louvain"},{"key":"2423_CR48","first-page":"375","volume":"167","author":"IK Hadihardaja","year":"2012","unstructured":"Hadihardaja IK et al (2012) Decision support system for predicting flood characteristics based on database modelling development (case study: Upper Citarum, West Java, Indonesia). WIT Trans Ecol Environ 167:375\u2013385","journal-title":"WIT Trans Ecol Environ"},{"key":"2423_CR49","unstructured":"Hallaq B et al (2017) Artificial intelligence within the military domain and cyber warfare. In: European conference on cyber warfare and security (ECCWS)"},{"issue":"2","key":"2423_CR50","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1177\/1094670517752459","volume":"21","author":"M-H Huang","year":"2018","unstructured":"Huang M-H, Rust RT (2018) Artificial intelligence in service. J Serv Res 21(2):155\u2013172. https:\/\/doi.org\/10.1177\/1094670517752459","journal-title":"J Serv Res"},{"issue":"2","key":"2423_CR51","doi-asserted-by":"publisher","first-page":"149","DOI":"10.14311\/NNW.2018.28.009","volume":"28","author":"J Huang","year":"2018","unstructured":"Huang J, Wang X, Zhao Y, Xin C, Xiang H (2018) Large earthquake magnitude prediction in Taiwan based on deep learning neural network. Neural Netw World 28(2):149\u2013160. https:\/\/doi.org\/10.14311\/NNW.2018.28.009","journal-title":"Neural Netw World"},{"key":"2423_CR52","doi-asserted-by":"crossref","unstructured":"Huang Q et al (2015) DisasterMapper: a CyberGIS framework for disaster management using social media data. In: Proceedings of the 4th international ACM SIGSPATIAL workshop on analytics for big geospatial data","DOI":"10.1145\/2835185.2835189"},{"issue":"5","key":"2423_CR53","doi-asserted-by":"publisher","first-page":"A4014004","DOI":"10.1061\/(ASCE)CP.1943-5487.0000334","volume":"28","author":"MM Torok","year":"2014","unstructured":"Torok MM, Golparvar-Fard M, Kochersberger KB (2014b) Image-based automated 3D crack detection for post-disaster building assessment. J Comput Civ Eng 28(5):A4014004. https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000334","journal-title":"J Comput Civ Eng"},{"key":"2423_CR54","doi-asserted-by":"publisher","unstructured":"Imran M, Castillo C, Lucas J, Meier P, Vieweg S (2014) AIDR: artificial intelligence for disaster response. In: WWW \u201914 companion. Association for Computing Machinery, pp 159\u2013162. https:\/\/doi.org\/10.1145\/2567948.2577034.","DOI":"10.1145\/2567948.2577034"},{"key":"2423_CR55","unstructured":"James Vincent (2016) Twitter taught Microsoft\u2019s AI chatbot to be a racist asshole in less than a day. www.theverge.com"},{"issue":"5","key":"2423_CR56","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.3390\/su10051651","volume":"10","author":"S Jeong","year":"2018","unstructured":"Jeong S, Yoon DK (2018) Examining vulnerability factors to natural disasters with a spatial autoregressive model: the case of South Korea. Sustainability 10(5):1651","journal-title":"Sustainability"},{"issue":"2","key":"2423_CR57","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1080\/14751798.2019.1600800","volume":"35","author":"J Johnson","year":"2019","unstructured":"Johnson J (2019) Artificial intelligence & future warfare: implications for international security. Defense Secur Anal 35(2):147\u2013169","journal-title":"Defense Secur Anal"},{"key":"2423_CR58","doi-asserted-by":"crossref","unstructured":"Keim M, Giannone P (2006) Disaster preparedness. In: Disaster medicine, Mosby, Philadelphia, PA, pp 164\u2013173","DOI":"10.1016\/B978-0-323-03253-7.50032-7"},{"issue":"6","key":"2423_CR59","doi-asserted-by":"publisher","first-page":"377","DOI":"10.5055\/jem.2016.0302","volume":"14","author":"MS Kiatpanont Rungsun","year":"2016","unstructured":"Kiatpanont Rungsun MS, Tanlamai Uthai P, Chongstitvatana Prabhas P (2016) Extraction of actionable information from crowdsourced disaster data. J Emerg Manag 14(6):377. https:\/\/doi.org\/10.5055\/jem.2016.0302","journal-title":"J Emerg Manag"},{"key":"2423_CR60","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.3390\/su12104021","volume":"12","author":"S-K Kim","year":"2020","unstructured":"Kim S-K, Huh J-H (2020) Blockchain of carbon trading for UN sustainable development goals. Sustainability 12:4021","journal-title":"Sustainability"},{"issue":"5","key":"2423_CR61","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MIS.2016.27","volume":"31","author":"S Kim","year":"2016","unstructured":"Kim S, Kim H, Namkoong Y (2016) Ordinal classification of imbalanced data with application in emergency and disaster information services. IEEE Intell Syst 31(5):50\u201356. https:\/\/doi.org\/10.1109\/MIS.2016.27","journal-title":"IEEE Intell Syst"},{"issue":"4","key":"2423_CR62","doi-asserted-by":"publisher","first-page":"510","DOI":"10.1002\/rob.21502","volume":"31","author":"K Kochersberger","year":"2014","unstructured":"Kochersberger K, Kroeger K, Krawiec B, Brewer E, Weber T (2014) Post-disaster remote sensing and sampling via an autonomous helicopter. J Field Robot 31(4):510\u2013521. https:\/\/doi.org\/10.1002\/rob.21502","journal-title":"J Field Robot"},{"issue":"8","key":"2423_CR63","doi-asserted-by":"publisher","first-page":"846","DOI":"10.3390\/app7080846","volume":"7","author":"S Kuang","year":"2017","unstructured":"Kuang S, Davison BD (2017) Learning word embeddings with chi-square weights for healthcare tweet classification. Appl Sci 7(8):846. https:\/\/doi.org\/10.3390\/app7080846","journal-title":"Appl Sci"},{"issue":"3","key":"2423_CR64","doi-asserted-by":"publisher","first-page":"23:1","DOI":"10.1145\/3383314","volume":"13","author":"P Kumar","year":"2020","unstructured":"Kumar P, Ofli F, Imran M, Castillo C (2020) Detection of disaster-affected cultural heritage sites from social media images using deep learning techniques. J Comput Cult Herit 13(3):23:1-23:31. https:\/\/doi.org\/10.1145\/3383314","journal-title":"J Comput Cult Herit"},{"key":"2423_CR65","volume-title":"Artificial intelligence and early warning systems. AI and robotics in disaster studies","author":"R Lamsal","year":"2020","unstructured":"Lamsal R, Kumar TV (2020) Artificial intelligence and early warning systems. AI and robotics in disaster studies. Palgrave Macmillan, Singapore"},{"issue":"1","key":"2423_CR66","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.ipm.2016.09.002","volume":"53","author":"F Laylavi","year":"2017","unstructured":"Laylavi F, Rajabifard A, Kalantari M (2017) Event relatedness assessment of Twitter messages for emergency response. Inf Process Manag 53(1):266\u2013280. https:\/\/doi.org\/10.1016\/j.ipm.2016.09.002","journal-title":"Inf Process Manag"},{"issue":"1","key":"2423_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/j.1681-4835.2010.tb00300.x","volume":"42","author":"J Li","year":"2010","unstructured":"Li J, Rao HR (2010a) Twitter as a rapid response news service: an exploration in the context of the 2008 China earthquake. Electron J Inf Syst Dev Count 42(1):1\u201322. https:\/\/doi.org\/10.1002\/j.1681-4835.2010.tb00300.x","journal-title":"Electron J Inf Syst Dev Count"},{"issue":"1","key":"2423_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/j.1681-4835.2010.tb00300.x","volume":"42","author":"J Li","year":"2010","unstructured":"Li J, Rao HR (2010b) Twitter as a rapid response news service: an exploration in the context of the 2008 China earthquake. Electron J Inf Syst Dev Countr 42(1):1\u201322. https:\/\/doi.org\/10.1002\/j.1681-4835.2010.tb00300.x","journal-title":"Electron J Inf Syst Dev Countr"},{"key":"2423_CR69","first-page":"2700","volume":"339","author":"A Liberati","year":"2009","unstructured":"Liberati A et al (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 339:2700","journal-title":"J Clin Epidemiol"},{"issue":"1","key":"2423_CR70","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCOM.2011.5681015","volume":"49","author":"K Mase","year":"2011","unstructured":"Mase K (2011) How to deliver your message from\/to a disaster area. IEEE Commun Mag 49(1):52\u201357. https:\/\/doi.org\/10.1109\/MCOM.2011.5681015","journal-title":"IEEE Commun Mag"},{"issue":"1","key":"2423_CR71","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1109\/MSP.2013.135","volume":"13","author":"M Maybury","year":"2015","unstructured":"Maybury M (2015) Toward the assured cyberspace advantage: air force cyber vision 2025. IEEE Secur Priv 13(1):49\u201356. https:\/\/doi.org\/10.1109\/MSP.2013.135","journal-title":"IEEE Secur Priv"},{"key":"2423_CR72","doi-asserted-by":"publisher","unstructured":"Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we RT? In: SOMA \u201910. Association for Computing Machinery, pp 71\u201379. https:\/\/doi.org\/10.1145\/1964858.1964869","DOI":"10.1145\/1964858.1964869"},{"issue":"12","key":"2423_CR73","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1080\/17538947.2020.1729879","volume":"13","author":"VV Mihunov","year":"2020","unstructured":"Mihunov VV, Lam NSN, Zou L, Wang Z, Wang K (2020) Use of Twitter in disaster rescue: lessons learned from Hurricane Harvey. Int J Digit Earth 13(12):1454\u20131466. https:\/\/doi.org\/10.1080\/17538947.2020.1729879","journal-title":"Int J Digit Earth"},{"issue":"3","key":"2423_CR74","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1007\/s11069-017-2934-z","volume":"88","author":"I Mitsopoulos","year":"2017","unstructured":"Mitsopoulos I, Mallinis G (2017) A data-driven approach to assess large fire size generation in Greece. Nat Hazards 88(3):1591\u20131607. https:\/\/doi.org\/10.1007\/s11069-017-2934-z","journal-title":"Nat Hazards"},{"key":"2423_CR75","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.4236\/jwarp.2012.412118","volume":"4","author":"P Mittal","year":"2012","unstructured":"Mittal P et al (2012) Dual artificial neural network for rainfall-runoff forecasting. J Water Resour Prot 4:1024\u20131028","journal-title":"J Water Resour Prot"},{"issue":"3","key":"2423_CR76","doi-asserted-by":"publisher","first-page":"68","DOI":"10.3390\/drones3030068","volume":"3","author":"ME Mohammadi","year":"2009","unstructured":"Mohammadi ME, Watson DP, Wood RL (2009) Deep learning-based damage detection from aerial SfM point clouds. Drones 3(3):68","journal-title":"Drones"},{"key":"2423_CR77","unstructured":"Morgan FE et al (2020) Military applications of artificial intelligence: ethical concerns in an uncertain world. Rand Project Air Force Santa Monica Ca Santa Monica United States. https:\/\/apps.dtic.mil\/sti\/citations\/AD1097313"},{"issue":"11","key":"2423_CR78","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.3390\/w10111536","volume":"10","author":"A Mosavi","year":"2018","unstructured":"Mosavi A, Ozturk P, Chau K (2018) Flood prediction using machine learning models: literature review. Water (Basel) 10(11):1536. https:\/\/doi.org\/10.3390\/w10111536","journal-title":"Water (Basel)"},{"issue":"2","key":"2423_CR79","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1109\/MRA.2009.932521","volume":"16","author":"RR Murphy","year":"2009","unstructured":"Murphy RR, Kravitz J, Stover SL, Shoureshi R (2009) Mobile robots in mine rescue and recovery. IEEE Robot Autom Mag 16(2):91\u2013103. https:\/\/doi.org\/10.1109\/MRA.2009.932521","journal-title":"IEEE Robot Autom Mag"},{"issue":"6","key":"2423_CR80","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1111\/mice.12359","volume":"33","author":"MA Nabian","year":"2018","unstructured":"Nabian MA, Meidani H (2018) Deep learning for accelerated seismic reliability analysis of transportation networks. Comput-Aid Civ Infrastruct Eng 33(6):443\u2013458. https:\/\/doi.org\/10.1111\/mice.12359","journal-title":"Comput-Aid Civ Infrastruct Eng"},{"issue":"1","key":"2423_CR81","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1002\/rob.21439","volume":"30","author":"K Nagatani","year":"2013","unstructured":"Nagatani K et al (2013) Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots. J Field Robot 30(1):44\u201363. https:\/\/doi.org\/10.1002\/rob.21439","journal-title":"J Field Robot"},{"key":"2423_CR82","doi-asserted-by":"crossref","unstructured":"Nagatani K et al (2013) Volcanic ash observation in active volcano areas using teleoperated mobile robots-introduction to our robotic-volcano-observation project and field experiments. In: IEEE international symposium on safety, security, and rescue robotics (SSRR)","DOI":"10.1109\/SSRR.2013.6719324"},{"key":"2423_CR83","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.resconrec.2014.02.008","volume":"86","author":"A Neshat","year":"2014","unstructured":"Neshat A, Pradhan B, Dadras M (2014) Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resour Conserv Recycl 86:74\u201386. https:\/\/doi.org\/10.1016\/j.resconrec.2014.02.008","journal-title":"Resour Conserv Recycl"},{"key":"2423_CR84","unstructured":"Norris A (2015) Disaster E-health: a new paradigm for collaborative healthcare in disasters. In: ISCRAM"},{"key":"2423_CR85","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.procir.2020.02.167","volume":"91","author":"OI Olayode","year":"2020","unstructured":"Olayode OI, Tartibu LK, Okwu MO (2020) Application of artificial intelligence in traffic control system of non-autonomous vehicles at signalized road intersection. Procedia CIRP 91:194\u2013200. https:\/\/doi.org\/10.1016\/j.procir.2020.02.167","journal-title":"Procedia CIRP"},{"key":"2423_CR86","unstructured":"Ortiz B et al (2020) Improving community resiliency and emergency response with artificial intelligence. arXiv:2005.14212 [cs]"},{"key":"2423_CR87","doi-asserted-by":"crossref","unstructured":"Perry RW (2018) Defining disaster: An evolving concept. Handbook of disaster research, 3-22.","DOI":"10.1007\/978-3-319-63254-4_1"},{"key":"2423_CR88","unstructured":"Washington Post (2022) Teslas running Autopilot involved in 273 crashes reported since last year"},{"key":"2423_CR89","doi-asserted-by":"publisher","DOI":"10.5812\/traumamon.80528","author":"HR Rasouli","year":"2018","unstructured":"Rasouli HR, Zahedi HR, Abbasi Farajzadeh M, Aliakbar Esfahani A, Ahmadpour F (2018) Medical aspects of earthquakes in Iran. Trauma Mon. https:\/\/doi.org\/10.5812\/traumamon.80528","journal-title":"Trauma Mon"},{"key":"2423_CR90","unstructured":"Rauter M, Winkler D (2018) Predicting natural hazards with neuronal networks. arXiv:1802.07257"},{"key":"2423_CR91","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1002\/rob.21756","volume":"35","author":"CT Recchiuto","year":"2018","unstructured":"Recchiuto CT, Sgorbissa A (2018) Post-disaster assessment with unmanned aerial vehicles: a survey on practical implementations and research approaches. J Field Robot 35:459\u2013490","journal-title":"J Field Robot"},{"key":"2423_CR92","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.trd.2019.03.002","volume":"77","author":"D Reynard","year":"2018","unstructured":"Reynard D, Shirgaokar M (2018) Harnessing the power of machine learning: can Twitter data be useful in guiding resource allocation decisions during a natural disaster? Transp Res D Transp Environ 77:449\u2013463. https:\/\/doi.org\/10.1016\/j.trd.2019.03.002","journal-title":"Transp Res D Transp Environ"},{"key":"2423_CR93","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/j.trd.2019.03.002","volume":"77","author":"D Reynard","year":"2019","unstructured":"Reynard D, Shirgaokar M (2019) Harnessing the power of machine learning: can Twitter data be useful in guiding resource allocation decisions during a natural disaster? Transp Res D Transp Environ 77:449\u2013463. https:\/\/doi.org\/10.1016\/j.trd.2019.03.002","journal-title":"Transp Res D Transp Environ"},{"key":"2423_CR94","unstructured":"Ritchie H, Roser M (2019) Natural Disasters. Our World in Data. https:\/\/ourworldindata.org\/natural-disasters#deaths-from-disasters."},{"key":"2423_CR95","doi-asserted-by":"publisher","unstructured":"Rizk Y, Jomaa HS, Awad M, Castillo C (2019) A computationally efficient multi-modal classification approach of disaster-related Twitter images. In: SAC \u201919. Association for Computing Machinery, pp 2050\u20132059. https:\/\/doi.org\/10.1145\/3297280.3297481.","DOI":"10.1145\/3297280.3297481"},{"key":"2423_CR96","doi-asserted-by":"crossref","unstructured":"Roy C (2016) An informed system development approach to tropical cyclone track and intensity forecasting. http:\/\/urn.kb.se\/resolve?urn=urn:nbn:se:liu:diva-123198","DOI":"10.3384\/diss.diva-123198"},{"key":"2423_CR97","doi-asserted-by":"crossref","unstructured":"Ruslan FA, Zain ZM, Adnan R (2013) Flood prediction using NARX neural network and EKF prediction technique: a comparative study. In: IEEE 3rd international conference on system engineering and technology","DOI":"10.1109\/ICSEngT.2013.6650171"},{"key":"2423_CR98","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.future.2020.01.005","volume":"106","author":"GA Ruz","year":"2020","unstructured":"Ruz GA, Henr\u00edquez PA, Mascare\u00f1o A (2020) Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Fut Gener Comput Syst 106:92\u2013104. https:\/\/doi.org\/10.1016\/j.future.2020.01.005","journal-title":"Fut Gener Comput Syst"},{"issue":"5","key":"2423_CR99","doi-asserted-by":"publisher","first-page":"973","DOI":"10.3390\/w11050973","volume":"11","author":"S Saravi","year":"2019","unstructured":"Saravi S, Kalawsky R, Joannou D, Rivas Casado M, Fu G, Meng F (2019) Use of artificial intelligence to improve resilience and preparedness against adverse flood events. Water (Basel) 11(5):973. https:\/\/doi.org\/10.3390\/w11050973","journal-title":"Water (Basel)"},{"key":"2423_CR100","doi-asserted-by":"crossref","unstructured":"Schofield M (2022) An Artificial Intelligence (AI) Approach to Controlling Disaster Scenarios. In Future Role of Sustainable Innovative Technologies in Crisis Management (pp. 28-46). IGI Global Scientific Publishing.","DOI":"10.4018\/978-1-7998-9815-3.ch003"},{"key":"2423_CR101","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1108\/09653560710837000","volume":"16","author":"IM Shaluf","year":"2007","unstructured":"Shaluf IM (2007) An overview on disasters. Disast Prev Manag Int J 16:687\u2013703","journal-title":"Disast Prev Manag Int J"},{"key":"2423_CR102","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-030-87897-9_32","volume-title":"Lecture notes in computer science","author":"AMJ Skulimowski","year":"2021","unstructured":"Skulimowski AMJ, Ba\u00f1uls VA (2021) AI alignment of disaster resilience management support systems. In: Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM (eds) Lecture notes in computer science. Springer International Publishing, Berlin, pp 354\u2013366. https:\/\/doi.org\/10.1007\/978-3-030-87897-9_32"},{"key":"2423_CR103","doi-asserted-by":"publisher","unstructured":"Soden R, Wagenaar D, Luo D, Tijssen A (2019) Taking ethics, fairness, and bias seriously in machine learning for disaster risk management. https:\/\/doi.org\/10.48550\/arXiv.1912.05538","DOI":"10.48550\/arXiv.1912.05538"},{"issue":"1958","key":"2423_CR104","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1098\/rsta.2011.0244","volume":"370","author":"M Srivastava","year":"2012","unstructured":"Srivastava M, Abdelzaher T, Szymanski B (2012) Human-centric sensing. Philos Trans R Soc A Math Phys Eng Sci 370(1958):176\u2013197. https:\/\/doi.org\/10.1098\/rsta.2011.0244","journal-title":"Philos Trans R Soc A Math Phys Eng Sci"},{"key":"2423_CR105","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23919\/OCEANS.2009.5422201","volume":"2009","author":"ET Steimle","year":"2009","unstructured":"Steimle ET, Murphy RR, Lindemuth M, Hall ML (2009) Unmanned marine vehicle use at Hurricanes Wilma and Ike. Oceans 2009:1\u20136. https:\/\/doi.org\/10.23919\/OCEANS.2009.5422201","journal-title":"Oceans"},{"issue":"3","key":"2423_CR106","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1007\/s11069-020-04124-3","volume":"103","author":"W Sun","year":"2020","unstructured":"Sun W, Bocchini P, Davison BD (2020a) Applications of artificial intelligence for disaster management. Nat Hazards 103(3):2631\u20132689. https:\/\/doi.org\/10.1007\/s11069-020-04124-3","journal-title":"Nat Hazards"},{"issue":"3","key":"2423_CR107","doi-asserted-by":"publisher","first-page":"2631","DOI":"10.1007\/s11069-020-04124-3","volume":"103","author":"W Sun","year":"2020","unstructured":"Sun W, Bocchini P, Davison BD (2020b) Applications of artificial intelligence for disaster management. Nat Hazards 103(3):2631\u20132689. https:\/\/doi.org\/10.1007\/s11069-020-04124-3","journal-title":"Nat Hazards"},{"key":"2423_CR108","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.chb.2015.04.020","volume":"50","author":"B Takahashi","year":"2015","unstructured":"Takahashi B, Tandoc EC, Carmichael C (2015) Communicating on Twitter during a disaster: an analysis of tweets during Typhoon Haiyan in the Philippines. Comput Human Behav 50:392\u2013398. https:\/\/doi.org\/10.1016\/j.chb.2015.04.020","journal-title":"Comput Human Behav"},{"key":"2423_CR109","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1007\/s11069-020-04429-3","volume":"107","author":"L Tan","year":"2021","unstructured":"Tan L, Guo J, Mohanarajah S, Zhou K (2021) Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices. Nat Hazards 107:2389\u20132417","journal-title":"Nat Hazards"},{"key":"2423_CR110","unstructured":"The New York Times (2020) Driver charged in Uber\u2019s fatal 2018 autonomous car crash"},{"key":"2423_CR111","unstructured":"Thomson R et al (2012) Trusting tweets: the Fukushima disaster and information source credibility on Twitter. p 10. files\/8262\/Thomson et al.-2012-Trusting Tweets The Fukushima Disaster and Inform.pdf"},{"issue":"5","key":"2423_CR112","doi-asserted-by":"publisher","first-page":"A4014004","DOI":"10.1061\/(ASCE)CP.1943-5487.0000334","volume":"28","author":"MM Torok","year":"2014","unstructured":"Torok MM, Golparvar-Fard M, Kochersberger KB (2014a) Image-based automated 3D crack detection for post-disaster building assessment. J Comput Civ Eng 28(5):A4014004. https:\/\/doi.org\/10.1061\/(ASCE)CP.1943-5487.0000334","journal-title":"J Comput Civ Eng"},{"issue":"01","key":"2423_CR113","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1142\/S268998092450009X","volume":"05","author":"J Visave","year":"2024","unstructured":"Visave J (2024) AI in emergency management: ethical considerations and challenges. J Emerg Manag Disaster Commun 05(01):165\u2013183. https:\/\/doi.org\/10.1142\/S268998092450009X","journal-title":"J Emerg Manag Disaster Commun"},{"key":"2423_CR114","unstructured":"World Health Organization (WHO) 14.9 million excess deaths associated with the COVID-19 pandemic in 2020 and 2021. May 2022"},{"key":"2423_CR115","unstructured":"World Meteorological Organization (2021) Atlas of Mortality and Economic Losses from Weather, Climate and Water-related Hazards. WMO. https:\/\/wmo.int\/publication-series\/atlas-of-mortality-and-economic-losses-from-weather-climate-and-water-related-hazards-1970-2021."},{"key":"2423_CR116","unstructured":"WorldOMeters (2022). https:\/\/www.worldometers.info\/coronavirus\/. May 2022"},{"issue":"3\u20134","key":"2423_CR117","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1504\/IJEP.2006.011211","volume":"28","author":"CL Wu","year":"2006","unstructured":"Wu CL, Chau KW (2006) A flood forecasting neural network model with genetic algorithm. Int J Environ Pollut 28(3\u20134):261\u2013273. https:\/\/doi.org\/10.1504\/IJEP.2006.011211","journal-title":"Int J Environ Pollut"},{"issue":"8","key":"2423_CR118","doi-asserted-by":"publisher","first-page":"e2111997119","DOI":"10.1073\/pnas.2111997119","volume":"119","author":"T Yabe","year":"2022","unstructured":"Yabe T, Rao PSC, Ukkusuri SV, Cutter SL (2022a) Toward data-driven, dynamical complex systems approaches to disaster resilience. Proc Natl Acad Sci 119(8):e2111997119. https:\/\/doi.org\/10.1073\/pnas.2111997119","journal-title":"Proc Natl Acad Sci"},{"issue":"5","key":"2423_CR119","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.3390\/jtaer16050107","volume":"16","author":"N Yoon","year":"2021","unstructured":"Yoon N, Lee H-K (2021) AI recommendation service acceptance: assessing the effects of perceived empathy and need for cognition. J Theor Appl Electron Commer Res 16(5):1912\u20131928. https:\/\/doi.org\/10.3390\/jtaer16050107","journal-title":"J Theor Appl Electron Commer Res"},{"issue":"4","key":"2423_CR120","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1504\/IJEM.2013.059879","volume":"9","author":"AT Zagorecki","year":"2013","unstructured":"Zagorecki AT, Johnson DEA, Ristvej J (2013) Data mining and machine learning in the context of disaster and crisis management. Int J Emerg Manag 9(4):351\u2013365. https:\/\/doi.org\/10.1504\/IJEM.2013.059879","journal-title":"Int J Emerg Manag"},{"issue":"5","key":"2423_CR121","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1080\/24694452.2017.1421897","volume":"108","author":"L Zou","year":"2018","unstructured":"Zou L, Lam NSN, Cai H, Qiang Y (2018) Mining twitter data for improved understanding of disaster resilience. Ann Am Assoc Geogr 108(5):1422\u20131441. https:\/\/doi.org\/10.1080\/24694452.2017.1421897","journal-title":"Ann Am Assoc Geogr"}],"updated-by":[{"DOI":"10.1007\/s00146-025-02612-3","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000}},{"DOI":"10.1007\/s00146-026-03212-5","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000}}],"container-title":["AI &amp; SOCIETY"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00146-025-02423-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00146-025-02423-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00146-025-02423-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:11:59Z","timestamp":1784304719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00146-025-02423-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,27]]},"references-count":120,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["2423"],"URL":"https:\/\/doi.org\/10.1007\/s00146-025-02423-6","relation":{},"ISSN":["0951-5666","1435-5655"],"issn-type":[{"value":"0951-5666","type":"print"},{"value":"1435-5655","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,27]]},"assertion":[{"value":"31 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The original online version of this article was revised to update error in the paragraph  text \"The results of the search were 240 relevant papers\".","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":8,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":9,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":10,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00146-025-02612-3","URL":"https:\/\/doi.org\/10.1007\/s00146-025-02612-3","order":11,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2026","order":12,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":13,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":14,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00146-026-03212-5","URL":"https:\/\/doi.org\/10.1007\/s00146-026-03212-5","order":15,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}