Explainable Multi-Granularity Attribution Reasoning Framework for Fake News Detection

Explainable Multi-Granularity Attribution Reasoning Framework for Fake News 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 Article Explainable Multi-Granularity Attribution Reasoning Framework for Fake News Detection Wei Ji, Hongzhen Lv, Hanbin Zhao, Roger Zimmermann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7601010/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Fake news detection has garnered the attention of an increasing number of researchers in recent years, particularly in the context of multimodal fake news that combines text and images. However, existing methods only focus on cross-modal feature fusion guided by a consistency matrix and make predictions based on shallow semantic modeling. This type of method relies on the invisible interaction of internal features to output results, and thus has a strong black-box characteristic. Furthermore, the meticulously fabricated fake news that exists in the real world is heterogeneous. In order to improve the influence of fake news, forgers will package it in many ways to deceive the existing detectors. To address the above issues, we first investigate the hidden psychological motivations behind fake news, and propose an Explainable Multi-granularity Attribution Reasoning framework for Fake News Detection, named EMAR-FND. Specifically, four fine-grained hierarchical reasoning networks are included in the proposed framework. They perform attribution reasoning for fake news from different perspectives, aiming to find subtle manipulation details. Finally, the intermediate layer features from different perspectives are aggregated through a multi-granularity information fusion module. Experimental results demonstrate that our proposed EMAR-FND outperforms existing state-of-the-art fake news detection methods under the same settings. Furthermore, we further verify the explainability of the proposed model through a discrimination performance experiment. Physical sciences/Engineering Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Explainable AI Fake news detection Attribution reasoning Social network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 31 Oct, 2025 Reviews received at journal 31 Oct, 2025 Reviews received at journal 31 Oct, 2025 Reviewers agreed at journal 31 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 30 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 12 Sep, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7601010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":538012323,"identity":"17aeaf4c-08d0-4ed1-aa2e-c71b4a69fabe","order_by":0,"name":"Wei Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYFACxgaDhAoGZhBTggQtZ0jTAtLVBqGJ02JwvLmh4OG8O+wGB5gP3uZhsMsjrOXMwQaDxG3PmA0OsCVb8zAkFxPUYnYjEaTlMFALj5k0D8OBxAaCWu4/BGqZA9LC/41ILTeAIZbYALaFjTgt9meADks4dphZ8jCbseUcg2TCWiTbjz8z/FFzOJnvePPDG28q7AhrAQI2AyCRDIlMAyLUAwHzAyBhR5zaUTAKRsEoGJEAAF5XPH1tRsHmAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Ji","suffix":""},{"id":538012324,"identity":"bdb49210-25d4-41ae-9b62-9d563ae1e139","order_by":1,"name":"Hongzhen Lv","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Hongzhen","middleName":"","lastName":"Lv","suffix":""},{"id":538012325,"identity":"3c36ff78-38b3-4040-a5df-a22039ce92a2","order_by":2,"name":"Hanbin Zhao","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Hanbin","middleName":"","lastName":"Zhao","suffix":""},{"id":538012326,"identity":"9873d42e-53af-44e2-9047-0bbb3962e7fe","order_by":3,"name":"Roger Zimmermann","email":"","orcid":"","institution":"National University of Singapore","correspondingAuthor":false,"prefix":"","firstName":"Roger","middleName":"","lastName":"Zimmermann","suffix":""}],"badges":[],"createdAt":"2025-09-12 13:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7601010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7601010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95585542,"identity":"d974cfe5-d9e7-49ff-a313-9b8775d4d781","added_by":"auto","created_at":"2025-11-10 23:13:08","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6296,"visible":true,"origin":"","legend":"","description":"","filename":"5aaff6c2dfda4d8dad1c70e47a6fb7ef.json","url":"https://assets-eu.researchsquare.com/files/rs-7601010/v1/dfc9f6e7c5c16cf4379a7e08.json"},{"id":95655739,"identity":"df2c48d4-0892-4d86-ac82-c1f35fd5e689","added_by":"auto","created_at":"2025-11-11 16:16:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2288682,"visible":true,"origin":"","legend":"","description":"","filename":"ExplainableMultiGranularityAttributionReasoningFrameworkforFakeNewsDetection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7601010/v1_covered_4d4ebadc-b930-4c65-bf1e-391dbd36b5f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable Multi-Granularity Attribution Reasoning Framework for Fake News Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Artificial Intelligence](https://www.nature.com/npjai)","snPcode":"443878","submissionUrl":"https://submission.springernature.com/new-submission/443878/3","title":"npj Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Explainable AI, Fake news detection, Attribution reasoning, Social network","lastPublishedDoi":"10.21203/rs.3.rs-7601010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7601010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Fake news detection has garnered the attention of an increasing number of researchers in recent years, particularly in the context of multimodal fake news that combines text and images. However, existing methods only focus on cross-modal feature fusion guided by a consistency matrix and make predictions based on shallow semantic modeling. This type of method relies on the invisible interaction of internal features to output results, and thus has a strong black-box characteristic. Furthermore, the meticulously fabricated fake news that exists in the real world is heterogeneous. In order to improve the influence of fake news, forgers will package it in many ways to deceive the existing detectors. To address the above issues, we first investigate the hidden psychological motivations behind fake news, and propose an Explainable Multi-granularity Attribution Reasoning framework for Fake News Detection, named EMAR-FND. Specifically, four fine-grained hierarchical reasoning networks are included in the proposed framework. They perform attribution reasoning for fake news from different perspectives, aiming to find subtle manipulation details. Finally, the intermediate layer features from different perspectives are aggregated through a multi-granularity information fusion module. Experimental results demonstrate that our proposed EMAR-FND outperforms existing state-of-the-art fake news detection methods under the same settings. Furthermore, we further verify the explainability of the proposed model through a discrimination performance experiment.","manuscriptTitle":"Explainable Multi-Granularity Attribution Reasoning Framework for Fake News Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 23:13:03","doi":"10.21203/rs.3.rs-7601010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T09:46:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T10:52:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T10:05:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-31T07:26:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285008966953259157400347442695489212409","date":"2025-10-31T05:53:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294870748012141843581388888887640588029","date":"2025-10-30T16:24:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250065642747190931338155543093426476878","date":"2025-10-30T13:46:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-30T13:00:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T03:06:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T10:52:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Artificial Intelligence","date":"2025-09-12T13:13:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Artificial Intelligence](https://www.nature.com/npjai)","snPcode":"443878","submissionUrl":"https://submission.springernature.com/new-submission/443878/3","title":"npj Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b6f1620-fe96-4217-ab32-6020e06a186f","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57220565,"name":"Physical sciences/Engineering"},{"id":57220566,"name":"Physical sciences/Mathematics and computing"},{"id":57220567,"name":"Biological sciences/Psychology"},{"id":57220568,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-09T04:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 23:13:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7601010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7601010","identity":"rs-7601010","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}