{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AANWMHE77WNGF7IXTWAX5PFSWL","short_pith_number":"pith:AANWMHE7","schema_version":"1.0","canonical_sha256":"001b661c9ffd9a62fd179d817ebcb2b2d69514b19b6d39a3dabe8cb4c29e206e","source":{"kind":"arxiv","id":"2408.09939","version":2},"attestation_state":"computed","paper":{"title":"\"Image, Tell me your story!\" Predicting the original meta-context of visual misinformation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Jonathan Tonglet, Marie-Francine Moens","submitted_at":"2024-08-19T12:21:34Z","abstract_excerpt":"To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in the image. However, they neglect a crucial point of the human fact-checking process: identifying the original meta-context of the image. By explaining what is actually true about the image, fact-checkers can better detect misinformation, focus their efforts on check-worthy visual content, engage in counter-messaging before misinformation spreads wide"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2408.09939","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-19T12:21:34Z","cross_cats_sorted":[],"title_canon_sha256":"88be24987fa57ef4a3c6bea4150779f952d99c7ab38c337b1ce1804fde1ee7d3","abstract_canon_sha256":"1607f8ad77e13dbbc949fe9df5199ab1703c014a56370ee5dd68a78901fa0d41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:07.291705Z","signature_b64":"CsGrpjt831Y81SE++J8kaknOEwaAJAFviB6YvgfBQkuZEz8G8mx4Sqsb3vNrw+Si8XaATLYvTXKK0ov7rGEyDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"001b661c9ffd9a62fd179d817ebcb2b2d69514b19b6d39a3dabe8cb4c29e206e","last_reissued_at":"2026-07-05T08:57:07.291151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:07.291151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"\"Image, Tell me your story!\" Predicting the original meta-context of visual misinformation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Iryna Gurevych, Jonathan Tonglet, Marie-Francine Moens","submitted_at":"2024-08-19T12:21:34Z","abstract_excerpt":"To assist human fact-checkers, researchers have developed automated approaches for visual misinformation detection. These methods assign veracity scores by identifying inconsistencies between the image and its caption, or by detecting forgeries in the image. However, they neglect a crucial point of the human fact-checking process: identifying the original meta-context of the image. By explaining what is actually true about the image, fact-checkers can better detect misinformation, focus their efforts on check-worthy visual content, engage in counter-messaging before misinformation spreads wide"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.09939","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2408.09939/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.09939","created_at":"2026-07-05T08:57:07.291222+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.09939v2","created_at":"2026-07-05T08:57:07.291222+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.09939","created_at":"2026-07-05T08:57:07.291222+00:00"},{"alias_kind":"pith_short_12","alias_value":"AANWMHE77WNG","created_at":"2026-07-05T08:57:07.291222+00:00"},{"alias_kind":"pith_short_16","alias_value":"AANWMHE77WNGF7IX","created_at":"2026-07-05T08:57:07.291222+00:00"},{"alias_kind":"pith_short_8","alias_value":"AANWMHE7","created_at":"2026-07-05T08:57:07.291222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.10166","citing_title":"Fact-Checking with Contextual Narratives: Leveraging Retrieval-Augmented LLMs for Social Media Analysis","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL","json":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL.json","graph_json":"https://pith.science/api/pith-number/AANWMHE77WNGF7IXTWAX5PFSWL/graph.json","events_json":"https://pith.science/api/pith-number/AANWMHE77WNGF7IXTWAX5PFSWL/events.json","paper":"https://pith.science/paper/AANWMHE7"},"agent_actions":{"view_html":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL","download_json":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL.json","view_paper":"https://pith.science/paper/AANWMHE7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.09939&json=true","fetch_graph":"https://pith.science/api/pith-number/AANWMHE77WNGF7IXTWAX5PFSWL/graph.json","fetch_events":"https://pith.science/api/pith-number/AANWMHE77WNGF7IXTWAX5PFSWL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL/action/storage_attestation","attest_author":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL/action/author_attestation","sign_citation":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL/action/citation_signature","submit_replication":"https://pith.science/pith/AANWMHE77WNGF7IXTWAX5PFSWL/action/replication_record"}},"created_at":"2026-07-05T08:57:07.291222+00:00","updated_at":"2026-07-05T08:57:07.291222+00:00"}