{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WWHRVWHINQ56WBP3X7BYHPS4W5","short_pith_number":"pith:WWHRVWHI","schema_version":"1.0","canonical_sha256":"b58f1ad8e86c3beb05fbbfc383be5cb7640d0f89ba6831dba47dabf5e10a63b5","source":{"kind":"arxiv","id":"2201.07472","version":1},"attestation_state":"computed","paper":{"title":"Detecting Stance in Tweets : A Signed Network based Approach","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Joydeep Chandra, Maitry Bhavsar, Roshni Chakraborty, Sourav Kumar Dandapat","submitted_at":"2022-01-19T08:49:42Z","abstract_excerpt":"Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require "},"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":"2201.07472","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2022-01-19T08:49:42Z","cross_cats_sorted":[],"title_canon_sha256":"0409904d512ecac3185676f4332eb44a5af13f20395459fde20e03fea20fa378","abstract_canon_sha256":"c4b5520db51e95b105ab01ec7dc497ab9bf0e15e745c63b9f32ae109dc4b8eb3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:49:46.897556Z","signature_b64":"NRyz4Hite2m0+6bC/JfqlNpnqr0Cg5SEtanrSTBZhUW8PqNgTF38XU2u0yBnuGhuHBenc6dnUryG2seu6JwsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b58f1ad8e86c3beb05fbbfc383be5cb7640d0f89ba6831dba47dabf5e10a63b5","last_reissued_at":"2026-07-05T03:49:46.897132Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:49:46.897132Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Stance in Tweets : A Signed Network based Approach","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Joydeep Chandra, Maitry Bhavsar, Roshni Chakraborty, Sourav Kumar Dandapat","submitted_at":"2022-01-19T08:49:42Z","abstract_excerpt":"Identifying user stance related to a political event has several applications, like determination of individual stance, shaping of public opinion, identifying popularity of government measures and many others. The huge volume of political discussions on social media platforms, like, Twitter, provide opportunities in developing automated mechanisms to identify individual stance and subsequently, scale to a large volume of users. However, issues like short text and huge variance in the vocabulary of the tweets make such exercise enormously difficult. Existing stance detection algorithms require "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.07472","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/2201.07472/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":"2201.07472","created_at":"2026-07-05T03:49:46.897189+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.07472v1","created_at":"2026-07-05T03:49:46.897189+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.07472","created_at":"2026-07-05T03:49:46.897189+00:00"},{"alias_kind":"pith_short_12","alias_value":"WWHRVWHINQ56","created_at":"2026-07-05T03:49:46.897189+00:00"},{"alias_kind":"pith_short_16","alias_value":"WWHRVWHINQ56WBP3","created_at":"2026-07-05T03:49:46.897189+00:00"},{"alias_kind":"pith_short_8","alias_value":"WWHRVWHI","created_at":"2026-07-05T03:49:46.897189+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23412","citing_title":"EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5","json":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5.json","graph_json":"https://pith.science/api/pith-number/WWHRVWHINQ56WBP3X7BYHPS4W5/graph.json","events_json":"https://pith.science/api/pith-number/WWHRVWHINQ56WBP3X7BYHPS4W5/events.json","paper":"https://pith.science/paper/WWHRVWHI"},"agent_actions":{"view_html":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5","download_json":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5.json","view_paper":"https://pith.science/paper/WWHRVWHI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.07472&json=true","fetch_graph":"https://pith.science/api/pith-number/WWHRVWHINQ56WBP3X7BYHPS4W5/graph.json","fetch_events":"https://pith.science/api/pith-number/WWHRVWHINQ56WBP3X7BYHPS4W5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5/action/storage_attestation","attest_author":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5/action/author_attestation","sign_citation":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5/action/citation_signature","submit_replication":"https://pith.science/pith/WWHRVWHINQ56WBP3X7BYHPS4W5/action/replication_record"}},"created_at":"2026-07-05T03:49:46.897189+00:00","updated_at":"2026-07-05T03:49:46.897189+00:00"}