{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XVLVTBZA2BKQAVHCYYZNYSAPGQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a473deb176ca240d50dc0705d73242ca131a516c77c1c8277f8c894dd74933cc","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-18T17:01:49Z","title_canon_sha256":"1146321087ee1f12e4425587a60b04d39b8545b387bfc79c74f7e5bf9fb8207c"},"schema_version":"1.0","source":{"id":"2204.08405","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.08405","created_at":"2026-07-05T04:15:25Z"},{"alias_kind":"arxiv_version","alias_value":"2204.08405v1","created_at":"2026-07-05T04:15:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08405","created_at":"2026-07-05T04:15:25Z"},{"alias_kind":"pith_short_12","alias_value":"XVLVTBZA2BKQ","created_at":"2026-07-05T04:15:25Z"},{"alias_kind":"pith_short_16","alias_value":"XVLVTBZA2BKQAVHC","created_at":"2026-07-05T04:15:25Z"},{"alias_kind":"pith_short_8","alias_value":"XVLVTBZA","created_at":"2026-07-05T04:15:25Z"}],"graph_snapshots":[{"event_id":"sha256:caff2bb7c8ce345bc76e4d3097fc43ca371d62c80ec0a5a10ca2a3d9620f85b2","target":"graph","created_at":"2026-07-05T04:15:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2204.08405/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Online news and social media have been the de facto mediums to disseminate information globally from the beginning of the last decade. However, bias in content and purpose of intentions are not regulated, and managing bias is the responsibility of content consumers. In this regard, understanding the stances and biases of news sources towards specific entities becomes important. To address this problem, we use pretrained language models, which have been shown to bring about good results with no task-specific training or few-shot training. In this work, we approach the problem of characterizing ","authors_text":"Kumari Neha, Nishchay Malakar, Ponnurangam Kumaraguru, Sharath Srivatsa, Srinath Srinivasa, Tushar Mohan","cross_cats":["cs.IR"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-18T17:01:49Z","title":"Zero-shot Entity and Tweet Characterization with Designed Conditional Prompts and Contexts"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08405","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6f4ab6558661b68c8d0f232b0b8da7e1502f60460a3872a3ee5d4d2bedd10ab7","target":"record","created_at":"2026-07-05T04:15:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a473deb176ca240d50dc0705d73242ca131a516c77c1c8277f8c894dd74933cc","cross_cats_sorted":["cs.IR"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-18T17:01:49Z","title_canon_sha256":"1146321087ee1f12e4425587a60b04d39b8545b387bfc79c74f7e5bf9fb8207c"},"schema_version":"1.0","source":{"id":"2204.08405","kind":"arxiv","version":1}},"canonical_sha256":"bd57598720d0550054e2c632dc480f343e7805c4030d469447497584d233d515","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd57598720d0550054e2c632dc480f343e7805c4030d469447497584d233d515","first_computed_at":"2026-07-05T04:15:25.649857Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:15:25.649857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C6qR9p95TtAubJLlv8dWlrh8DSBisxwk77t1YgAyGNvtOPZHOaU+2NKQJkEiuMPOBRwAKkDSJEWCEtmhW/GlAw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:15:25.650357Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.08405","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f4ab6558661b68c8d0f232b0b8da7e1502f60460a3872a3ee5d4d2bedd10ab7","sha256:caff2bb7c8ce345bc76e4d3097fc43ca371d62c80ec0a5a10ca2a3d9620f85b2"],"state_sha256":"4820a60ea2a5fc1260c94b0089503f29e28339dc16772273f02a2546aef30749"}