{"paper":{"title":"Spectral Analysis of Fake News Propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Spectral bounds on graph propagation patterns distinguish fake news cascades from real ones and enable classification plus structural interpretation.","cross_cats":["cs.AI"],"primary_cat":"cs.SI","authors_text":"Reza Zafarani, Weibin Cai","submitted_at":"2026-04-18T14:55:54Z","abstract_excerpt":"The propagation structure of fake news has been shown to be an important cue for detecting it; yet, existing propagation-based fake news detection methods have mainly relied on ad hoc topological features, and a unified view of cascade patterns is still lacking. To address this, we study news propagation from a spectral view by connecting graph spectra to propagation-related structural properties through rigorous spectral bounds. In particular, we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. We then use th"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"we introduce several new bounds and integrate them with existing ones into a unified spectral representation of information propagation. 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We then use these spectral bounds for downstream classification and design a discrete structural optimization framework to interpret learned propagation patterns.","one_line_summary":"New spectral bounds create a unified representation of news cascade structures that supports competitive fake news detection and interpretable optimization of propagation patterns.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the derived spectral bounds and first-order perturbation approximation faithfully capture the distinguishing structural properties of real versus fake news cascades without substantial information loss or approximation error that would invalidate downstream classification and interpretation.","pith_extraction_headline":"Spectral bounds on graph propagation patterns distinguish fake news cascades from real ones and enable classification plus structural interpretation."},"references":{"count":19,"sample":[{"doi":"","year":null,"title":"maximum of out degree","work_id":"0bad2e0c-acf2-4346-a4cc-b140be535f3f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"depth of the maximum of out degree node","work_id":"38532bfe-69b0-46bc-923c-38832461a99e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"width entropy: P i p(wi) log(p(wi)),p(w i) = wiP i wi , wherew i is the width of depthi","work_id":"ed4d7ea8-7fc7-4b5d-b448-2d2423448a90","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Category Meaning / Property Bound Equation C1","work_id":"32015878-bbc2-4a9e-82b8-b7f7d382b483","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"mean branching: P i∈I(T) di |I(T)| , where|I(T)|is the number of internal nodes,di is the degree of nodei","work_id":"37b802d9-c6cc-450f-80f0-10248d86ba3b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":19,"snapshot_sha256":"169cb21a71c9897688dc15aedcde720225d8bec99cba83065c701528145dd1a5","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}