{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WAWUOWHLV3YG3CYZOVAWKC73V6","short_pith_number":"pith:WAWUOWHL","schema_version":"1.0","canonical_sha256":"b02d4758ebaef06d8b197541650bfbaf8ae3cf1c6e9d8f62c99e1a4e08280621","source":{"kind":"arxiv","id":"1801.06482","version":1},"attestation_state":"computed","paper":{"title":"Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.SI"],"primary_cat":"cs.IR","authors_text":"Amit Awekar, Sweta Agrawal","submitted_at":"2018-01-19T16:27:36Z","abstract_excerpt":"Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring"},"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":"1801.06482","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2018-01-19T16:27:36Z","cross_cats_sorted":["cs.CL","cs.SI"],"title_canon_sha256":"770fb1b5a26deb0adcbbd3058a29c7c7977d26a4ae732aed8f4884515d07ade5","abstract_canon_sha256":"ca84d431c0e5103bb7f02133be219b12fb8814d1c73128b75f23984175d76616"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:29.915941Z","signature_b64":"GTnunRlmQlJn5lnkc4ROnHy/WEjOuf+MpgTaRZ7wSQrTyI465k0Av+rcHzXX/T6w9nzwfBjHG1Sas4yPXEr6DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b02d4758ebaef06d8b197541650bfbaf8ae3cf1c6e9d8f62c99e1a4e08280621","last_reissued_at":"2026-05-18T00:25:29.915169Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:29.915169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.SI"],"primary_cat":"cs.IR","authors_text":"Amit Awekar, Sweta Agrawal","submitted_at":"2018-01-19T16:27:36Z","abstract_excerpt":"Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.06482","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":""},"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":"1801.06482","created_at":"2026-05-18T00:25:29.915294+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.06482v1","created_at":"2026-05-18T00:25:29.915294+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.06482","created_at":"2026-05-18T00:25:29.915294+00:00"},{"alias_kind":"pith_short_12","alias_value":"WAWUOWHLV3YG","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WAWUOWHLV3YG3CYZ","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WAWUOWHL","created_at":"2026-05-18T12:32:59.047623+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/WAWUOWHLV3YG3CYZOVAWKC73V6","json":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6.json","graph_json":"https://pith.science/api/pith-number/WAWUOWHLV3YG3CYZOVAWKC73V6/graph.json","events_json":"https://pith.science/api/pith-number/WAWUOWHLV3YG3CYZOVAWKC73V6/events.json","paper":"https://pith.science/paper/WAWUOWHL"},"agent_actions":{"view_html":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6","download_json":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6.json","view_paper":"https://pith.science/paper/WAWUOWHL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.06482&json=true","fetch_graph":"https://pith.science/api/pith-number/WAWUOWHLV3YG3CYZOVAWKC73V6/graph.json","fetch_events":"https://pith.science/api/pith-number/WAWUOWHLV3YG3CYZOVAWKC73V6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6/action/storage_attestation","attest_author":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6/action/author_attestation","sign_citation":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6/action/citation_signature","submit_replication":"https://pith.science/pith/WAWUOWHLV3YG3CYZOVAWKC73V6/action/replication_record"}},"created_at":"2026-05-18T00:25:29.915294+00:00","updated_at":"2026-05-18T00:25:29.915294+00:00"}