[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116992-en":3,"doc-seo-116992-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},116992,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine Learning Methods for Evaluating Public Crisis - Meta-Analysis","This study examines how machine learning supports crisis management by extracting and evaluating patterns from historical and near real-time datasets using automated processes. A meta-review method synthesizes peer-reviewed literature that applies machine learning to assess human actions during crises. Studies are grouped into themes and emerging trends via systematic review of papers from three scholarly databases. Social media data dominates usage (27%), and supervised learning is most prevalent (69%).","arXiv :2302 .02267v 1 [ cs .LG] 5 Feb 2023  \nIEEE Copyright Notice  \nCopyright (c) 2022 IEEE  \nPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.  \nAccepted to be published in: 9th Annual Conference on Computational Science & Computational Intelligence (CSCI'22: Dec 14-16, 2022, USA)  \nCite as:  \nO. Izunna, H. Shane, and K. Jess.“Machine Learning Methods for Evaluating Public Crisis: Meta-Analysis,” 2022 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2022 .  \nBibTeX:  \n@InProceedingsfokpala2022methods,  \nauthor = fOkpala, Izunna and Halse, Shane and Kropczynski, Jessg,  \ntitle = fMachine Learning Methods for Evaluating Public Crisis: Meta-Analysisg, booktitle = f2022 International Conference on Computational Science and Computational Intelligence (CSCI)g,  \nmonth = fDecember 14 – 16g,  \nyear = f2022g,  \npublisher = fIEEEg, g  \nMachine Learning Methods for Evaluating Public  \nCrisis: Meta-Analysis  \n1st Izunna Okpala School of Information Technology University of Cincinnati [okpalaiu@mail.uc.edu](okpalaiu@mail.uc.edu)  \n2nd Shane Halse School of Information Technology University of Cincinnati [halsese@ucmail.uc.edu](halsese@ucmail.uc.edu)  \n3rd Jess Kropczynski School of Information Technology University of Cincinnati [kropczjn@ucmail.uc.edu](kropczjn@ucmail.uc.edu)  \nAbstract—This study examines machine learning methods used in crisis management. Analyzing detected patterns from a crisis involves the collection and evaluation of historical or near-realtime datasets through automated means. This paper utilized the meta-review method to analyze scientific literature that utilized machine learning techniques to evaluate human actions during crises. Selected studies were condensed into themes and emerging trends using a systematic literature evaluation of published works accessed from three scholarly databases. Results show that data from social media was prominent in the evaluated articles with 27% usage, followed by disaster management, health (COVID) and crisis informatics, amongst many other themes. Additionally, the supervised machine learning method, with an application of 69% across the board, was predominant. The classification technique stood out among other machine learning tasks with 41% usage. The algorithms that played major roles were the Support Vector Machine, Neural Networks, Naive Bayes, and Random Forest, with 23%, 16%, 15%, and 12% contributions, respectively.  \nIndex Terms—Crisis informatics, Disaster management, Machine Learning, Learning Algorithms, Meta Analysis  \nI. INTRODUCTION  \nOver the last decade, the scientific community like IEEE and the Information Systems for Crisis Response and Management (ISCRAM) have contributed many studies that utilize realtime information sources to support situation awareness during large-scale events [1], [2] . The Machine learning field has advanced on how they automate processes to filter large volumes of data [3] . This study explores a variety of machine learning solutions utilized in scholarly articles to understand human actions towards crises. It was also informed by studies that addressed disaster management, health, politics, and other forms of crisis that utilized data beyond social media with evidential proof from many scholarly articles available in major academic databases that focused on analyzing human actions. While the first interactive medium for an individual that has no control over the mainstream media is the social media platform, local sources for tracking crises exist. People tend to report incidents, or debate about the ongoing incident via a social network that is familiar to th","cbCaieDmcWYN9Ls7","https://ap.wps.com/l/cbCaieDmcWYN9Ls7","pdf",683245,1,"English","en",105,"# Abstract\n# Introduction\n## Research Questions\n# Background","[{\"question\":\"Which data sources and learning methods are most frequently reported?\",\"answer\":\"Social media data is prominent in the evaluated articles (27%). Supervised machine learning is the predominant approach (69%) across the reviewed studies.\"}]","Machine Learning Methods for Evaluating Public Crisis - Meta-Analysis | PDF",1785672997,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"machine-learning-methods-for-evaluating-public-crisis-meta-analysis","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/machine-learning-methods-for-evaluating-public-crisis-meta-analysis/116992/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"Which data sources and learning methods are most frequently reported?","Question",{"text":74,"@type":75},"Social media data is prominent in the evaluated articles (27%). 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