{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:46:40Z","timestamp":1778086000996,"version":"3.51.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Belmont Forum\u2019s first disaster-focused funding Call Belmont Collaborative Research Action 2019","award":["DR32019"],"award-info":[{"award-number":["DR32019"]}]},{"name":"Belmont Forum\u2019s first disaster-focused funding Call Belmont Collaborative Research Action 2019","award":["DR32019"],"award-info":[{"award-number":["DR32019"]}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["EP\/V002945\/1"],"award-info":[{"award-number":["EP\/V002945\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["EP\/V002945\/1"],"award-info":[{"award-number":["EP\/V002945\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper presents a social media data analysis framework applied to multiple datasets. The method developed uses machine learning classifiers, where filtering binary classifiers based on deep bidirectional neural networks are trained on benchmark datasets of disaster responses for earthquakes and floods and extreme flood events. The classifiers consist of learning from discrete handcrafted features and fine-tuning approaches using deep bidirectional Transformer neural networks on these disaster response datasets. With the development of the multiclass classification approach, we compare the state-of-the-art results in one of the benchmark datasets containing the largest number of disaster-related categories. The multiclass classification approaches developed in this research with support vector machines provide a precision of 0.83 and 0.79 compared to Bernoulli na\u00efve Bayes, which are 0.59 and 0.76, and multinomial na\u00efve Bayes, which are 0.79 and 0.91, respectively. The binary classification methods based on the MDRM dataset show a higher precision with deep learning methods (DistilBERT) than BoW and TF-IDF, while in the case of UnifiedCEHMET dataset show a high performance for accuracy with the deep learning method in terms of severity, with a precision of 0.92 compared to BoW and TF-IDF method which has a precision of 0.68 and 0.70, respectively.<\/jats:p>","DOI":"10.1007\/s44163-022-00026-4","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T12:03:56Z","timestamp":1654517036000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Social media data analysis framework for disaster response"],"prefix":"10.1007","volume":"2","author":[{"given":"V\u00edctor","family":"Ponce-L\u00f3pez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catalina","family":"Spataru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"issue":"19","key":"26_CR1","doi-asserted-by":"publisher","first-page":"1836","DOI":"10.1056\/NEJMra1109877","volume":"369","author":"J Leaning","year":"2013","unstructured":"Leaning J, Debarati GS. Natural disasters, armed conflict, and public health. New Engl J Med Public Health. 2013;369(19):1836\u201342.","journal-title":"New Engl J Med Public Health"},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/978-3-642-40837-3_7","volume":"1","author":"PM Landwehr","year":"2014","unstructured":"Landwehr PM, Carley KM. \u201cSocial media in disaster relief: usage patterns, data mining tools, and current research directions,\u201d data mining and knowledge discovery for big data. Studies in Big Data. 2014;1:225\u201357.","journal-title":"Studies in Big Data"},{"issue":"2","key":"26_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0210484","volume":"14","author":"MT Niles","year":"2019","unstructured":"Niles MT, Emery BF, Reagan AJ, Dodds PS, Danforth CM. Social media usage patterns during natural hazards. PLoS ONE. 2019;14(2):1\u201316.","journal-title":"PLoS ONE"},{"key":"26_CR4","unstructured":"CEH, UK Centre for Ecology & Hydrology. https:\/\/www.ceh.ac.uk\/."},{"key":"26_CR5","unstructured":"Metoffice, UK Meteorological Office, https:\/\/www.metoffice.gov.uk\/."},{"key":"26_CR6","unstructured":"Space and Naval Warfare Systems Center Atlantic, U.S. Department of Homeland Security (DHS), innovative uses of social media in emergency management: system assessment and validation for emergency responders (SAVER), 2013."},{"key":"26_CR7","volume-title":"Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises, Washington","author":"National Research Council","year":"2007","unstructured":"National Research Council. Tools and Methods for Estimating Populations at Risk from Natural Disasters and Complex Humanitarian Crises, Washington. DC: The National Academies Press; 2007."},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Poblet M, Garc\u00eda-Cuesta E, Casanovas P. Crowdsourcing tools for disaster management: a review of platforms and methods, In International Workshop on AI Approaches to the Complexity of Legal Systems, Berlin, Heidelberg, 2013.","DOI":"10.1007\/978-3-662-45960-7_19"},{"key":"26_CR9","unstructured":"Sphere Association, The Sphere Handbook: Humanitarian Charter and Minimum Standards in Humanitarian Response, fourth edition ed., Geneva, Switzerland: Core Humanitarian Standard on Quality and Accountability\u00a9 CHS Alliance, 2018."},{"key":"26_CR10","unstructured":"\u00d6zcan S. Tweet-Preprocessor. Available: https:\/\/pypi.org\/project\/tweet-preprocessor\/."},{"key":"26_CR11","unstructured":"Richardson L. Beautiful Soup Documentation. https:\/\/www.crummy.com\/software\/BeautifulSoup\/."},{"key":"26_CR12","unstructured":"Friedl J. Mastering regular expressions. 3rd ed., O\u2019Reilly Medi, 2009."},{"key":"26_CR13","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071","volume-title":"Introduction to Information Retrieval","author":"CD Manning","year":"2008","unstructured":"Manning CD, Raghavan P, Schuetze H. Introduction to Information Retrieval. Cambridge: Cambridge University Press; 2008."},{"key":"26_CR14","unstructured":"Platt JC. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. 1999."},{"key":"26_CR15","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"26_CR16","unstructured":"Chavda V. Tweet classification. https:\/\/github.com\/pointoflight\/tweet_classification."},{"key":"26_CR17","unstructured":"Bird S, Klein E, Loper E. Natural language processing with python, O'Reilly Media Inc, 2009."},{"key":"26_CR18","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, In 5th Workshop on Energy Efficient Machine Learning and Cognitive Computing, NeurIPS, 2019."},{"key":"26_CR19","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding, In Proceedings of North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2019."},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L. Deep Contextualized Word Representations. In Proceedings of NAACL-HLT, New Orleans, Louisiana, 2018.","DOI":"10.18653\/v1\/N18-1202"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Wang A, Tenney IF, Pruksachatkun Y, Yeres P, Phang J, Liu H, Htut PM, Yu K, Hula J, Xia P, Pappagari R, Jin S, McCoy RT, Patel R, Huang Y, Grave E, Kim N, F\u00e9vry T, Chen B, Nangia N, Mohananey A, Kann K, Bordia S, Patry N, Benton D, Pavlick E, Bowman SR. Jiant 1.3: A software toolkit for research on general-purpose text understanding models. 2019.","DOI":"10.18653\/v1\/2020.acl-demos.15"},{"key":"26_CR22","unstructured":"Hinton G, Vinyals O, Dean J. Distilling the Knowledge in a Neural Network. in NIPS Deep Learning and Representation Learning Workshop, 2015."},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Bucila C, Caruana R, Niculescu-Mzil A. Model compression, in KDD, 2006.","DOI":"10.1145\/1150402.1150464"},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi C, Cistac P, Rault T, Louf R, Funtowicz M, Davison J, Shleifer S, Platen Pv, Ma C, Jernite Y, Plu J, Xu C, Scao TL, Gugger S, Drame M. Lhoest Q, Rush A. Transformers: State-of-the-Art Natural Language Processing, in 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Wang A, Singh A, Michael J, Hill F, Levy O, Bowman SR, GLUE: a multi-task benchmark and analysis platform for natural language understanding. In Proceedings of the International Conference on Learning Representations (ICLR), 2019.","DOI":"10.18653\/v1\/W18-5446"},{"key":"26_CR26","unstructured":"Figure Eight. Social Media Disaster Tweets. https:\/\/www.figure-eight.com\/data-for-everyone\/."},{"key":"26_CR27","unstructured":"Appen. Multilingual Disaster Response Messages. https:\/\/appen.com\/datasets\/combined-disaster-response-data\/."},{"issue":"1","key":"26_CR28","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1080\/02626667.2014.950581","volume":"61","author":"AJ Stevens","year":"2016","unstructured":"Stevens AJ, Clarke D, Nicholls RJ. Trends in reported flooding in the UK: 1884\u20132013. Hydrol Sci J. 2016;61(1):50\u201363.","journal-title":"Hydrol Sci J"},{"key":"26_CR29","unstructured":"Ng L A Machine Learning Pipeline for Disaster Response. 2020. https:\/\/github.com\/lng15\/DisasterResponse."},{"issue":"4","key":"26_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5210\/fm.v17i4.3937","volume":"17","author":"A Bruns","year":"2012","unstructured":"Bruns A, Liang YE. Tools and methods for capturing Twitter data during natural disasters. First Monday. 2012;17(4):1\u20138.","journal-title":"First Monday"},{"key":"26_CR31","doi-asserted-by":"crossref","unstructured":"Imran M, Ofli F, Alam F. AIDR: Artificial Intelligence for Digital Response, Qatar Computing Research Institute, 2013. http:\/\/aidr.qcri.org\/.","DOI":"10.1145\/2567948.2577034"},{"key":"26_CR32","unstructured":"Spinn3r. API Documentation. 2016. http:\/\/docs.spinn3r.com\/."},{"key":"26_CR33","unstructured":"GNIP. Grand Central Station for the Social Web, ReadWriteWeb. 2008."},{"key":"26_CR34","doi-asserted-by":"crossref","unstructured":"S. Kumar, G. Barbier, M. Abbasi and H. Liu, \"TweetTracker: An Analysis Tool for Humanitarian and Disaster Relief,\" in Proceedings of the International AAAI Conference on Web and Social Media, 2021.","DOI":"10.1609\/icwsm.v5i1.14079"},{"key":"26_CR35","unstructured":"N. Altman, K. M. Carley and J. Reminga, \"ORA User's Guide 2020,\" CMU-ISR-20\u2013110, 2020."},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Carley KM. ORA: a toolkit for dynamic network analysis and visualization, RJ (Alhajj R., Ed., New York, NY: Encyclopedia of Social Network Analysis and Mining, Springer, 2014.","DOI":"10.1007\/978-1-4614-6170-8_309"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Ujawary-Gil A. Organizational network analysis: auditing intangible resources. 1st Edition. Routledge., 1st ed., Routledge, 2019.","DOI":"10.4324\/9780367408947-1"},{"key":"26_CR38","volume-title":"Social Radar. MITRE, McLean, Virginia, USA","author":"B Costa","year":"2012","unstructured":"Costa B, Boiney J. Social Radar. MITRE, McLean, Virginia, USA. McLean: The MITRE Corporation; 2012."},{"key":"26_CR39","volume-title":"Social Radar Workflows, Dashboards, and Environments","author":"J Mathieu","year":"2012","unstructured":"Mathieu J, Fulk M, Lorber MM, Klein G, Costa B, Schmorrow D. Social Radar Workflows, Dashboards, and Environments. Bedford: The MITRE Corporation; 2012."},{"key":"26_CR40","doi-asserted-by":"crossref","unstructured":"Schmerl B, Garlan D, Dwivedi V, Bigrigg MW, Carley KM. SORASCS: a case study in SOA-based platform design for socio-cultural analysis. In Proceedings of the 33rd International Conference on Software Engineering, Waikiki, Honolulu, 2011.","DOI":"10.1145\/1985793.1985883"},{"key":"26_CR41","unstructured":"Garlan D, Schmerl B, Dwivedi V, Bigrigg MW, Carley K. Specifying Workflows in SORASCS to Automate and Share Common HSCB Processes. In Proceedings of the HSCB Focus 2011: Integrating Social Science Theory and Analytic Methods for Operational Use, Chantilly, VA, 2011."}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00026-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-022-00026-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-022-00026-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T11:28:50Z","timestamp":1675769330000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-022-00026-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["26"],"URL":"https:\/\/doi.org\/10.1007\/s44163-022-00026-4","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"assertion":[{"value":"17 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"10"}}