{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:JU2XFLWQVDUMMHS7VTIGVR6WLU","short_pith_number":"pith:JU2XFLWQ","schema_version":"1.0","canonical_sha256":"4d3572aed0a8e8c61e5facd06ac7d65d32ec4c62378e2bcc749322fe5bccfc02","source":{"kind":"arxiv","id":"1710.07394","version":2},"attestation_state":"computed","paper":{"title":"Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexis Kuppersmith, Lei Gao, Ruihong Huang","submitted_at":"2017-10-20T02:11:06Z","abstract_excerpt":"In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals m"},"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":"1710.07394","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-20T02:11:06Z","cross_cats_sorted":[],"title_canon_sha256":"2d1fe4ce701b38c191f80c3bf1f8cbc703ab5376d18a501447d0a2db5d1b39db","abstract_canon_sha256":"6fb8b7533c76db45faf161e84e0c59646e9ee09bf2d1ef57d0114547aa5c168f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:30.495041Z","signature_b64":"19zzrNPbvwDP0xulavV56K7dWmNcDP4mljYRlbWGzL2LLU1X36efB4DDFQ9qGBDfdIU0X5BnAgkDe3SwwBLtBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d3572aed0a8e8c61e5facd06ac7d65d32ec4c62378e2bcc749322fe5bccfc02","last_reissued_at":"2026-05-18T00:15:30.494475Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:30.494475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexis Kuppersmith, Lei Gao, Ruihong Huang","submitted_at":"2017-10-20T02:11:06Z","abstract_excerpt":"In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07394","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1710.07394","created_at":"2026-05-18T00:15:30.494556+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.07394v2","created_at":"2026-05-18T00:15:30.494556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07394","created_at":"2026-05-18T00:15:30.494556+00:00"},{"alias_kind":"pith_short_12","alias_value":"JU2XFLWQVDUM","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"JU2XFLWQVDUMMHS7","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"JU2XFLWQ","created_at":"2026-05-18T12:31:24.725408+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/JU2XFLWQVDUMMHS7VTIGVR6WLU","json":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU.json","graph_json":"https://pith.science/api/pith-number/JU2XFLWQVDUMMHS7VTIGVR6WLU/graph.json","events_json":"https://pith.science/api/pith-number/JU2XFLWQVDUMMHS7VTIGVR6WLU/events.json","paper":"https://pith.science/paper/JU2XFLWQ"},"agent_actions":{"view_html":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU","download_json":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU.json","view_paper":"https://pith.science/paper/JU2XFLWQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.07394&json=true","fetch_graph":"https://pith.science/api/pith-number/JU2XFLWQVDUMMHS7VTIGVR6WLU/graph.json","fetch_events":"https://pith.science/api/pith-number/JU2XFLWQVDUMMHS7VTIGVR6WLU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU/action/storage_attestation","attest_author":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU/action/author_attestation","sign_citation":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU/action/citation_signature","submit_replication":"https://pith.science/pith/JU2XFLWQVDUMMHS7VTIGVR6WLU/action/replication_record"}},"created_at":"2026-05-18T00:15:30.494556+00:00","updated_at":"2026-05-18T00:15:30.494556+00:00"}