Multi-source Domain Adaptation Approach to Classify Infrastructure Damage Tweets during Crisis

Multi-source Domain Adaptation Approach to Classify Infrastructure Damage Tweets during Crisis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-source Domain Adaptation Approach to Classify Infrastructure Damage Tweets during Crisis Shalini Priya, Manish Bhanu, Saswata Roy, Sourav Kumar Dandapat, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4868198/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 9 You are reading this latest preprint version Abstract Instant crisis information on social media is the most searched essen-tials to provide relief and rescue operations to the victims at the early crisis hours. However, insufficient information on underway crisis incidents and rich data from past crisis events resort to domain adaptation (DA) techniques over any other approaches. Despite, the existing DA methods could not adequately engage the available past resources and hence lose vital information for the ongoing crisis incidents compromising the performance. Existing pitfalls of state-of-the-art models are: (1) models do not work on joint domain feature relation at elementary and instance level to exploit the complete information of each domain (2) moreover, these models could not efficiently harness the information, when there are diversified and varying number of source crisis incidents. Inspired by the ensemble setup in identifying the infrastructure damage, we introduce Ensemble model using the elementary feature (Parts-of-speech tagging) Attention and Hypersphere Separator Springer Nature 2021 L A T E X template Domain Adaptation Approach to Classify Infrastructure Damage (EnPHyS). It operates at joint feature levels where each level works with the abundant source and scarce target data to extract the best of the (1) shared and (2) invariant features for the objective task. Ensemble uses Multi-Task Learning (MTL) and an adversarial approach to enhance the information retrieval of target features. EnPHyS performance was investigated under single-source as well as multi-source domain adaptation scenarios with four publicly available datasets. The reported results on standard metric F-measure reveal the average growth of 17%, 22% and 38% respectively over the best performing baseline model. Domain adaptation Crisis informatics Ensemble DNN Elementary level Instance level N-dimensional hyper-sphere Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2025 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 18 Oct, 2024 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 24 Sep, 2024 Reviews received at journal 12 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers invited by journal 23 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 06 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4868198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346312385,"identity":"f31329b2-0f1c-4c22-92c4-a20bcab52f59","order_by":0,"name":"Shalini 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