{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:29:03Z","timestamp":1772044143478,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T00:00:00Z","timestamp":1688688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Effectively harnessing the power of social media data for disaster management requires sophisticated analysis methods and frameworks. This research focuses on understanding the contextual information present in social media posts during disasters and developing a taxonomy to effectively categorize and classify the diverse range of topics discussed. First, the existing literature on social media analysis in disaster management is explored, highlighting the limitations and gaps in current methodologies. Second, a dataset comprising real-time social media posts related to various disasters is collected and preprocessed to ensure data quality and reliability. Third, three well-established topic modeling techniques, namely Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-Negative Matrix Factorization (NMF), are employed to extract and analyze the latent topics and themes present in the social media data. The contributions of this research lie in the development of a taxonomy that effectively categorizes and classifies disaster-related social media data, the identification of key latent topics and themes, and the extraction of valuable insights to support and enhance emergency management efforts. Overall, the findings of this research have the potential to transform the way emergency management and response are conducted by harnessing the power of social media data. By incorporating these insights into decision-making processes, emergency managers can make more informed and strategic choices, resulting in more efficient and effective emergency response strategies. This, in turn, leads to improved outcomes, better utilization of resources, and ultimately, the ability to save lives and mitigate the impacts of disasters.<\/jats:p>","DOI":"10.3390\/info14070385","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Unveiling Key Themes and Establishing a Hierarchical Taxonomy of Disaster-Related Tweets: A Text Mining Approach for Enhanced Emergency Management Planning"],"prefix":"10.3390","volume":"14","author":[{"given":"James","family":"Durham","sequence":"first","affiliation":[{"name":"Department of Computer Sciences and Electrical Engineering, Marshall University, Huntington, WV 25755, USA"}]},{"given":"Sudipta","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Marshall University, Huntington, WV 25755, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6012-5679","authenticated-orcid":false,"given":"Ammar","family":"Alzarrad","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Marshall University, Huntington, WV 25755, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107495","DOI":"10.1016\/j.cie.2021.107495","article-title":"Drone routing and optimization for post-disaster inspection","volume":"159","author":"Chowdhury","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1111\/isj.12114","article-title":"Digitally enabled disaster response: The emergence of social media as boundary objects in a flooding disaster","volume":"27","author":"Tim","year":"2016","journal-title":"Inf. Syst. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1111\/1468-5973.12212","article-title":"The effect of social media on the dynamics of (self) resilience during disasters: A literature review","volume":"26","author":"Jurgens","year":"2017","journal-title":"J. Contingencies Crisis Manag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.ijinfomgt.2015.07.001","article-title":"Socializing in emergencies\u2014A review of the use of social media in emergency situations","volume":"35","author":"Simon","year":"2015","journal-title":"Int. J. Inf. Manag."},{"key":"ref_5","unstructured":"Kaigo, M. (2012). Social Media Usage During Disasters and Social Capital: Twitter and the Great East Japan Earthquake, Keio University. Keio Communication Review."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1038\/s41597-019-0326-9","article-title":"A global database of historic and real-time flood events based on social media","volume":"6","author":"Jongman","year":"2019","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mora, H., P\u00e9rez-Delhoyo, R., Paredes-P\u00e9rez, J.F., and Moll\u00e1-Sirvent, R.A. (2018). Analysis of Social Networking Service Data for Smart Urban Planning. Sustainability, 10.","DOI":"10.3390\/su10124732"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"54595","DOI":"10.1109\/ACCESS.2019.2913340","article-title":"The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects","volume":"7","author":"Shah","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2771588","article-title":"Processing Social Media Messages in Mass Emergency: A Survey","volume":"47","author":"Imran","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MIS.2012.6","article-title":"Using Social Media to Enhance Emergency Situation Awareness","volume":"27","author":"Yin","year":"2012","journal-title":"IEEE Intell. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, M., Yang, C., and Li, Y. (2018). Big Data in Natural Disaster Management: A Review. Geosciences, 8.","DOI":"10.3390\/geosciences8050165"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/s13278-016-0410-5","article-title":"The social role of social media: The case of Chennai rains-2015","volume":"6","author":"Yadav","year":"2016","journal-title":"Soc. Netw. Anal. Min."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1007\/s00779-020-01476-2","article-title":"A study on topic models using LDA and Word2Vec in travel route recommendation: Focus on convergence travel and tours reviews","volume":"26","author":"Park","year":"2020","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.technovation.2017.01.001","article-title":"Applying LSA text mining technique in envisioning social impacts of emerging technologies: The case of drone technology","volume":"60\u201361","author":"Kwon","year":"2017","journal-title":"Technovation"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chowdhury, S., and Alzarrad, A. (2023). Applications of Text Mining in the Transportation Infrastructure Sector: A Review. Information, 14.","DOI":"10.3390\/info14040201"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"04022044","DOI":"10.1061\/(ASCE)CP.1943-5487.0001059","article-title":"Investigation of Critical Factors for Future-Proofed Transportation Infrastructure Planning Using Topic Modeling and Association Rule Mining","volume":"37","author":"Chowdhury","year":"2023","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.chb.2015.04.020","article-title":"Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines","volume":"50","author":"Takahashi","year":"2015","journal-title":"Comput. Hum. Behav."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s11948-013-9502-z","article-title":"Social Media in Disaster Risk Reduction and Crisis Management","volume":"20","author":"Alexander","year":"2013","journal-title":"Sci. Eng. Ethic"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Reuter, C., Ludwig, T., Kaufhold, M.-A., and Pipek, V. (2015, January 18\u201323). XHELP: Design of a Cross-platform Social-media Application to Support Volunteer Moderators in Disasters. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Republic of Korea.","DOI":"10.1145\/2702123.2702171"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1177\/1461444817706877","article-title":"A framework for analyzing digital volunteer contributions in emergent crisis response efforts","volume":"19","author":"Park","year":"2017","journal-title":"New Media Soc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1108\/JHLSCM-08-2019-0054","article-title":"Challenges in disaster relief operations: Evidence from the 2017 Kermanshah earthquake","volume":"11","author":"Maghsoudi","year":"2020","journal-title":"J. Humanit. Logist. Supply Chain Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.neucom.2016.06.045","article-title":"Data mining techniques in social media: A survey","volume":"214","author":"Injadat","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/2641190.2641195","article-title":"Mining Social Media with Social Theories: A Survey","volume":"15","author":"Tang","year":"2014","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Elsayed, M., Abdelwahab, A., and Ahdelkader, H. (2019, January 17). A Proposed Framework for Improving Analysis of Big Unstructured Data in Social Media. Proceedings of the 2019 14th International Conference on Computer Engineering and Systems (ICCES), Cairo, Egypt. Available online: https:\/\/www.researchgate.net\/publication\/348578774.","DOI":"10.1109\/ICCES48960.2019.9068154"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, Z., Huang, K., Wu, L., Zhong, Z., and Jiao, Z. (2022). Relational Graph Convolutional Network for Text-Mining-Based Accident Causal Classification. Appl. Sci., 12.","DOI":"10.3390\/app12052482"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.tranpol.2021.06.020","article-title":"Sustainability disclosure for container shipping: A text-mining approach","volume":"110","author":"Zhou","year":"2021","journal-title":"Transp. Policy"},{"key":"ref_27","unstructured":"Tirunagari, S. (2015). Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101053","DOI":"10.1016\/j.aei.2020.101053","article-title":"A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks","volume":"45","author":"Chu","year":"2020","journal-title":"Adv. Eng. Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.ajsl.2022.10.003","article-title":"A systematic literature review on humanitarian logistics using network analysis and topic modeling","volume":"38","author":"Kim","year":"2022","journal-title":"Asian J. Shipp. Logist."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Choi, D., and Song, B. (2018). Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis. Sustainability, 10.","DOI":"10.3390\/su10082810"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.knosys.2019.02.033","article-title":"Transportation sentiment analysis using word embedding and ontology-based topic modeling","volume":"174","author":"Ali","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","unstructured":"Chowdhury, S., and Zhu, J. (2022). Future-Proof Transportation Infrastructure through Proactive, Intelligent, and Public-involved Planning and Management, University of Maine."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hagras, M., Hassan, G., and Farag, N. (2017, January 17\u201319). Towards Natural Disasters Detection from Twitter Using Topic Modelling. Proceedings of the 2017 European Conference on Electrical Engineering and Computer Science (EECS), Bern, Switzerland.","DOI":"10.1109\/EECS.2017.57"},{"key":"ref_34","unstructured":"Kireyev, K., Palen, L., and Anderson, K.M. (2009). NIPS Workshop on Applications for Topic Models: Text and Beyond, NIPS Workshop."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., Imran, M., Alam, T., and Qazi, U. (2020, January 7\u201310). Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response. Proceedings of the IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), The Hague, The Netherlands.","DOI":"10.1109\/ASONAM49781.2020.9381294"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zou, Z., Gan, H., Huang, Q., Cai, T., and Cao, K. (2021). Disaster Image Classification by Fusing Multimodal Social Media Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100636"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Alam, F., Ofli, F., and Imran, M. (2018, January 25\u201328). CrisisMMD: Multimodal Twitter Datasets from Natural Disasters. Proceedings of the Twelfth International AAAI Conference on Web and Social Media, Palo Alto, CA, USA. Available online: https:\/\/ojs.aaai.org\/index.php\/ICWSM\/article\/view\/14983.","DOI":"10.1609\/icwsm.v12i1.14983"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abavisani, M., Wu, L., Hu, S., Tetreault, J., and Jaimes, A. (2020, January 13\u201319). Multimodal Categorization of Crisis Events in Social Media. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA. Available online: https:\/\/ieeexplore.ieee.org\/document\/9157116.","DOI":"10.1109\/CVPR42600.2020.01469"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nelli, F. (2018). Python Data Analytics: With Pandas, NumPy, and Matplotlib, Apress Media LLC. [2nd ed.].","DOI":"10.1007\/978-1-4842-3913-1"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/7\/385\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:07:45Z","timestamp":1760126865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/7\/385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,7]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["info14070385"],"URL":"https:\/\/doi.org\/10.3390\/info14070385","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,7]]}}}