{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3STG437D3X273SKAN4WICCQSHB","short_pith_number":"pith:3STG437D","schema_version":"1.0","canonical_sha256":"dca66e6fe3ddf5fdc9406f2c810a123879551127b905199698c7193d973e3006","source":{"kind":"arxiv","id":"2403.03550","version":1},"attestation_state":"computed","paper":{"title":"Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.HC"],"primary_cat":"cs.AI","authors_text":"Federico Germani, Giovanni Spitale, Nikola Biller-Andorno, Rasita Vinay","submitted_at":"2024-03-06T08:50:25Z","abstract_excerpt":"This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanc"},"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":"2403.03550","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-03-06T08:50:25Z","cross_cats_sorted":["cs.CY","cs.HC"],"title_canon_sha256":"fe62991eb0499ae9c0a92329f1c7b65a8b1d8e57904b6c37fc8cf8a4ca7b410f","abstract_canon_sha256":"761d1e02a03c68d78b1b755e06673b47aaf56122d44a4537432ce94ddcae1acb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:49.814847Z","signature_b64":"UqxnAxD/1LM/P2Hp6LPOYgtwELMpZTBgyY6CJe9xDVK/l9FYeuhX0/D6pc//JwIxhEPt/zJ1JRzrc5mZYa3aCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dca66e6fe3ddf5fdc9406f2c810a123879551127b905199698c7193d973e3006","last_reissued_at":"2026-07-05T07:52:49.814487Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:49.814487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CY","cs.HC"],"primary_cat":"cs.AI","authors_text":"Federico Germani, Giovanni Spitale, Nikola Biller-Andorno, Rasita Vinay","submitted_at":"2024-03-06T08:50:25Z","abstract_excerpt":"This study investigates the generation of synthetic disinformation by OpenAI's Large Language Models (LLMs) through prompt engineering and explores their responsiveness to emotional prompting. Leveraging various LLM iterations using davinci-002, davinci-003, gpt-3.5-turbo and gpt-4, we designed experiments to assess their success in producing disinformation. Our findings, based on a corpus of 19,800 synthetic disinformation social media posts, reveal that all LLMs by OpenAI can successfully produce disinformation, and that they effectively respond to emotional prompting, indicating their nuanc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03550","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/2403.03550/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":"2403.03550","created_at":"2026-07-05T07:52:49.814545+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03550v1","created_at":"2026-07-05T07:52:49.814545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03550","created_at":"2026-07-05T07:52:49.814545+00:00"},{"alias_kind":"pith_short_12","alias_value":"3STG437D3X27","created_at":"2026-07-05T07:52:49.814545+00:00"},{"alias_kind":"pith_short_16","alias_value":"3STG437D3X273SKA","created_at":"2026-07-05T07:52:49.814545+00:00"},{"alias_kind":"pith_short_8","alias_value":"3STG437D","created_at":"2026-07-05T07:52:49.814545+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.09600","citing_title":"Effective Red-Teaming of Policy-Adherent Agents","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB","json":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB.json","graph_json":"https://pith.science/api/pith-number/3STG437D3X273SKAN4WICCQSHB/graph.json","events_json":"https://pith.science/api/pith-number/3STG437D3X273SKAN4WICCQSHB/events.json","paper":"https://pith.science/paper/3STG437D"},"agent_actions":{"view_html":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB","download_json":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB.json","view_paper":"https://pith.science/paper/3STG437D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03550&json=true","fetch_graph":"https://pith.science/api/pith-number/3STG437D3X273SKAN4WICCQSHB/graph.json","fetch_events":"https://pith.science/api/pith-number/3STG437D3X273SKAN4WICCQSHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB/action/storage_attestation","attest_author":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB/action/author_attestation","sign_citation":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB/action/citation_signature","submit_replication":"https://pith.science/pith/3STG437D3X273SKAN4WICCQSHB/action/replication_record"}},"created_at":"2026-07-05T07:52:49.814545+00:00","updated_at":"2026-07-05T07:52:49.814545+00:00"}