{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LTC6T4KHSJZRKJFVMUD6KPCZAG","short_pith_number":"pith:LTC6T4KH","schema_version":"1.0","canonical_sha256":"5cc5e9f14792731524b56507e53c5901a06f2c210c8c848b5726cec38d97a2af","source":{"kind":"arxiv","id":"2409.14395","version":1},"attestation_state":"computed","paper":{"title":"Predicting User Stances from Target-Agnostic Information using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hong Zhang, Joseph Simons, Liang Ze Wong, Prasanta Bhattacharya, Siyuan Brandon Loh, Wei Gao","submitted_at":"2024-09-22T11:21:16Z","abstract_excerpt":"We investigate Large Language Models' (LLMs) ability to predict a user's stance on a target given a collection of his/her target-agnostic social media posts (i.e., user-level stance prediction). While we show early evidence that LLMs are capable of this task, we highlight considerable variability in the performance of the model across (i) the type of stance target, (ii) the prediction strategy and (iii) the number of target-agnostic posts supplied. Post-hoc analyses further hint at the usefulness of target-agnostic posts in providing relevant information to LLMs through the presence of both su"},"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":"2409.14395","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-09-22T11:21:16Z","cross_cats_sorted":[],"title_canon_sha256":"24bb1d8970c63650a3b26574a739d23ace5009beab809080af60d740ef1eee9b","abstract_canon_sha256":"77558c9ab72db1a3fa0fe9b4eea3182ae372db7cabd8a059ce5d7f14d6c534f6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:16.142120Z","signature_b64":"sM+S19/8QYvo681KSFqLdD/M3TQ/0433cGMAwulW4rSwT/V2S6xAfb2s7Eb5v2X5MejARC1S4ah57ckTP7UNBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc5e9f14792731524b56507e53c5901a06f2c210c8c848b5726cec38d97a2af","last_reissued_at":"2026-07-05T09:10:16.141616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:16.141616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Predicting User Stances from Target-Agnostic Information using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hong Zhang, Joseph Simons, Liang Ze Wong, Prasanta Bhattacharya, Siyuan Brandon Loh, Wei Gao","submitted_at":"2024-09-22T11:21:16Z","abstract_excerpt":"We investigate Large Language Models' (LLMs) ability to predict a user's stance on a target given a collection of his/her target-agnostic social media posts (i.e., user-level stance prediction). While we show early evidence that LLMs are capable of this task, we highlight considerable variability in the performance of the model across (i) the type of stance target, (ii) the prediction strategy and (iii) the number of target-agnostic posts supplied. Post-hoc analyses further hint at the usefulness of target-agnostic posts in providing relevant information to LLMs through the presence of both su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.14395","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/2409.14395/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":"2409.14395","created_at":"2026-07-05T09:10:16.141672+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.14395v1","created_at":"2026-07-05T09:10:16.141672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.14395","created_at":"2026-07-05T09:10:16.141672+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTC6T4KHSJZR","created_at":"2026-07-05T09:10:16.141672+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTC6T4KHSJZRKJFV","created_at":"2026-07-05T09:10:16.141672+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTC6T4KH","created_at":"2026-07-05T09:10:16.141672+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG","json":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG.json","graph_json":"https://pith.science/api/pith-number/LTC6T4KHSJZRKJFVMUD6KPCZAG/graph.json","events_json":"https://pith.science/api/pith-number/LTC6T4KHSJZRKJFVMUD6KPCZAG/events.json","paper":"https://pith.science/paper/LTC6T4KH"},"agent_actions":{"view_html":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG","download_json":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG.json","view_paper":"https://pith.science/paper/LTC6T4KH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.14395&json=true","fetch_graph":"https://pith.science/api/pith-number/LTC6T4KHSJZRKJFVMUD6KPCZAG/graph.json","fetch_events":"https://pith.science/api/pith-number/LTC6T4KHSJZRKJFVMUD6KPCZAG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG/action/storage_attestation","attest_author":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG/action/author_attestation","sign_citation":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG/action/citation_signature","submit_replication":"https://pith.science/pith/LTC6T4KHSJZRKJFVMUD6KPCZAG/action/replication_record"}},"created_at":"2026-07-05T09:10:16.141672+00:00","updated_at":"2026-07-05T09:10:16.141672+00:00"}