Emotion-Aware Multimodal Framework with Similarity-Guided Gating for Disaster Misinformation Detection

Emotion-Aware Multimodal Framework with Similarity-Guided Gating for Disaster Misinformation Detection | 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 Emotion-Aware Multimodal Framework with Similarity-Guided Gating for Disaster Misinformation Detection Feifan Wang, Feng Zhang, Jingge Wang, Haonan Yan, Chaohao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405470/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in International Journal of Data Science and Analytics → Version 1 posted 9 You are reading this latest preprint version Abstract The rapid dissemination of disaster-related misinformation on social media poses serious risks to public safety and data-driven crisis response. Detecting such content remains challenging because misleading posts often combine emotionally charged text with reused, irrelevant, or weakly related images. This multimodal and affective complexity limits the effectiveness of text-only models and reduces the reliability of existing multimodal approaches.This paper proposes an emotion-aware multimodal framework for disaster misinformation detection that jointly models text, images, emotional signals, and image–text similarity. The approach utilizes cross-modal attention and similarity-guided gating to regulate visual contributions based on semantic alignment, thereby enhancing robustness to misleading images.Experiments on the large-scale Fakeddit dataset show that the proposed method achieves 0.943 accuracy and 0.942 F1-score , outperforming multimodal baselines. When transferred to the Crisis dataset, it attains 0.895 accuracy and 0.873 F1-score , indicating effective cross-domain generalization. Additional robustness experiments confirm stability under modality degradation, while analysis of input features highlights the complementary roles of domain-specific emotional cues and domain-invariant image–text inconsistency.These results suggest the value of combining affective signals with alignment-aware multimodal analysis for reliable disaster misinformation detection. Multimodal fusion Disaster response Misinformation detection Social media Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in International Journal of Data Science and Analytics → Version 1 posted Editorial decision: Revision requested 26 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviews received at journal 25 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Submission checks completed at journal 19 Dec, 2025 First submitted to journal 19 Dec, 2025 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-8405470","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580781939,"identity":"fac31de0-aa82-4ff1-9170-c5924cb69b83","order_by":0,"name":"Feifan Wang","email":"","orcid":"","institution":"Hangzhou Research Institute, Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Feifan","middleName":"","lastName":"Wang","suffix":""},{"id":580781940,"identity":"ddaa4d69-28a3-4d80-98a7-bd57c7e60189","order_by":1,"name":"Feng Zhang","email":"","orcid":"","institution":"Zhejiang Key Laboratory of Artificial Intelligence 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Detecting such content remains challenging because misleading posts often combine emotionally charged text with reused, irrelevant, or weakly related images. This multimodal and affective complexity limits the effectiveness of text-only models and reduces the reliability of existing multimodal approaches.This paper proposes an emotion-aware multimodal framework for disaster misinformation detection that jointly models text, images, emotional signals, and image\u0026ndash;text similarity. The approach utilizes cross-modal attention and similarity-guided gating to regulate visual contributions based on semantic alignment, thereby enhancing robustness to misleading images.Experiments on the large-scale Fakeddit dataset show that the proposed method achieves \u003cb\u003e0.943 accuracy\u003c/b\u003e and \u003cb\u003e0.942 F1-score\u003c/b\u003e, outperforming multimodal baselines. 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Additional robustness experiments confirm stability under modality degradation, while analysis of input features highlights the complementary roles of domain-specific emotional cues and domain-invariant image\u0026ndash;text inconsistency.These results suggest the value of combining affective signals with alignment-aware multimodal analysis for reliable disaster misinformation detection.\u003c/p\u003e","manuscriptTitle":"Emotion-Aware Multimodal Framework with Similarity-Guided Gating for Disaster Misinformation Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 10:36:57","doi":"10.21203/rs.3.rs-8405470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-26T23:36:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T08:12:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T12:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339440908553949975707420958937213419337","date":"2026-01-22T12:59:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139909798275877920311252253175249927754","date":"2026-01-22T12:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T10:53:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T22:22:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-20T03:55:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Data Science and Analytics","date":"2025-12-19T13:48:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"118d8eae-4f0f-4e9a-b8dc-d758d44d503d","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:05:00+00:00","versionOfRecord":{"articleIdentity":"rs-8405470","link":"https://doi.org/10.1007/s41060-026-01060-6","journal":{"identity":"international-journal-of-data-science-and-analytics","isVorOnly":false,"title":"International Journal of Data Science and Analytics"},"publishedOn":"2026-03-05 15:59:19","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2026-01-27 10:36:57","video":"","vorDoi":"10.1007/s41060-026-01060-6","vorDoiUrl":"https://doi.org/10.1007/s41060-026-01060-6","workflowStages":[]},"version":"v1","identity":"rs-8405470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8405470","identity":"rs-8405470","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}