{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NWMYNRZGSQ5ALTOMJAWPVXP454","short_pith_number":"pith:NWMYNRZG","schema_version":"1.0","canonical_sha256":"6d9986c726943a05cdcc482cfaddfcef1307fe73e15f44e00feed747d7bf36f4","source":{"kind":"arxiv","id":"2403.12838","version":1},"attestation_state":"computed","paper":{"title":"How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Josh A. Goldstein, Renee DiResta","submitted_at":"2024-03-19T15:43:16Z","abstract_excerpt":"Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need f"},"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":"2403.12838","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2024-03-19T15:43:16Z","cross_cats_sorted":[],"title_canon_sha256":"3f2676f26256f5b9d7ba146d2b6cff1835fcd7c9ed0b8c669ffef6d31cc2c390","abstract_canon_sha256":"61768fd9bbc34b311a66be0e9a87b49f90a64f623b056d2f280d89bcd4c3cfd6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:58:08.461375Z","signature_b64":"0/+4LeuFTi3lSj2sqQsaRPgy5gCfbBiLYuybsuYattkaU2ZKNjib4zlNG+lpeSxpEraUT9lAUpZmaH3dKZptBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d9986c726943a05cdcc482cfaddfcef1307fe73e15f44e00feed747d7bf36f4","last_reissued_at":"2026-07-05T07:58:08.460895Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:58:08.460895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Spammers and Scammers Leverage AI-Generated Images on Facebook for Audience Growth","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CY","authors_text":"Josh A. Goldstein, Renee DiResta","submitted_at":"2024-03-19T15:43:16Z","abstract_excerpt":"Much of the research and discourse on risks from artificial intelligence (AI) image generators, such as DALL-E and Midjourney, has centered around whether they could be used to inject false information into political discourse. We show that spammers and scammers - seemingly motivated by profit or clout, not ideology - are already using AI-generated images to gain significant traction on Facebook. At times, the Facebook Feed is recommending unlabeled AI-generated images to users who neither follow the Pages posting the images nor realize that the images are AI-generated, highlighting the need f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12838","kind":"arxiv","version":1},"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/2403.12838/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":"2403.12838","created_at":"2026-07-05T07:58:08.460950+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.12838v1","created_at":"2026-07-05T07:58:08.460950+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12838","created_at":"2026-07-05T07:58:08.460950+00:00"},{"alias_kind":"pith_short_12","alias_value":"NWMYNRZGSQ5A","created_at":"2026-07-05T07:58:08.460950+00:00"},{"alias_kind":"pith_short_16","alias_value":"NWMYNRZGSQ5ALTOM","created_at":"2026-07-05T07:58:08.460950+00:00"},{"alias_kind":"pith_short_8","alias_value":"NWMYNRZG","created_at":"2026-07-05T07:58:08.460950+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26199","citing_title":"MIRAGE: Protecting against Malicious Image Editing via False Moderation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.26199","citing_title":"MIRAGE: Protecting against Malicious Image Editing via False Moderation","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2512.13915","citing_title":"Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10460","citing_title":"Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection","ref_index":9,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454","json":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454.json","graph_json":"https://pith.science/api/pith-number/NWMYNRZGSQ5ALTOMJAWPVXP454/graph.json","events_json":"https://pith.science/api/pith-number/NWMYNRZGSQ5ALTOMJAWPVXP454/events.json","paper":"https://pith.science/paper/NWMYNRZG"},"agent_actions":{"view_html":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454","download_json":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454.json","view_paper":"https://pith.science/paper/NWMYNRZG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.12838&json=true","fetch_graph":"https://pith.science/api/pith-number/NWMYNRZGSQ5ALTOMJAWPVXP454/graph.json","fetch_events":"https://pith.science/api/pith-number/NWMYNRZGSQ5ALTOMJAWPVXP454/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454/action/storage_attestation","attest_author":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454/action/author_attestation","sign_citation":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454/action/citation_signature","submit_replication":"https://pith.science/pith/NWMYNRZGSQ5ALTOMJAWPVXP454/action/replication_record"}},"created_at":"2026-07-05T07:58:08.460950+00:00","updated_at":"2026-07-05T07:58:08.460950+00:00"}