{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DZ6D2OVFZZ43QNMROO24XE4D7K","short_pith_number":"pith:DZ6D2OVF","schema_version":"1.0","canonical_sha256":"1e7c3d3aa5ce79b8359173b5cb9383fa9efc0f70b305b5a2e124a3bc59adf43a","source":{"kind":"arxiv","id":"1908.09919","version":2},"attestation_state":"computed","paper":{"title":"Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Erhan Sezerer, Ozan Polatbilek, Selma Tekir","submitted_at":"2019-08-22T07:36:48Z","abstract_excerpt":"Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn 'where to look'. This model (https://github.com/Darg-Iztech/gender-prediction-from-tweets) is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the p"},"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":"1908.09919","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-22T07:36:48Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"1bcb2f16b6a13446755b8d69542da7009518183885fad1920f15e71b47bba150","abstract_canon_sha256":"a25b4d2fda6baea0edcd94efabc05614286d163b9fec14b6c2212d6643823881"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:02:40.093694Z","signature_b64":"nLZhRnQilyE6Fd6c0aKIsV7Ln2OB8CVKlu7rTC0Olq3pS7p2DCsGijH6RaOeOm1Z1icog2yioQMxzd4X+1gPBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e7c3d3aa5ce79b8359173b5cb9383fa9efc0f70b305b5a2e124a3bc59adf43a","last_reissued_at":"2026-07-05T00:02:40.093246Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:02:40.093246Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Erhan Sezerer, Ozan Polatbilek, Selma Tekir","submitted_at":"2019-08-22T07:36:48Z","abstract_excerpt":"Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn 'where to look'. This model (https://github.com/Darg-Iztech/gender-prediction-from-tweets) is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09919","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.09919/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":"1908.09919","created_at":"2026-07-05T00:02:40.093320+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.09919v2","created_at":"2026-07-05T00:02:40.093320+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09919","created_at":"2026-07-05T00:02:40.093320+00:00"},{"alias_kind":"pith_short_12","alias_value":"DZ6D2OVFZZ43","created_at":"2026-07-05T00:02:40.093320+00:00"},{"alias_kind":"pith_short_16","alias_value":"DZ6D2OVFZZ43QNMR","created_at":"2026-07-05T00:02:40.093320+00:00"},{"alias_kind":"pith_short_8","alias_value":"DZ6D2OVF","created_at":"2026-07-05T00:02:40.093320+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/DZ6D2OVFZZ43QNMROO24XE4D7K","json":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K.json","graph_json":"https://pith.science/api/pith-number/DZ6D2OVFZZ43QNMROO24XE4D7K/graph.json","events_json":"https://pith.science/api/pith-number/DZ6D2OVFZZ43QNMROO24XE4D7K/events.json","paper":"https://pith.science/paper/DZ6D2OVF"},"agent_actions":{"view_html":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K","download_json":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K.json","view_paper":"https://pith.science/paper/DZ6D2OVF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.09919&json=true","fetch_graph":"https://pith.science/api/pith-number/DZ6D2OVFZZ43QNMROO24XE4D7K/graph.json","fetch_events":"https://pith.science/api/pith-number/DZ6D2OVFZZ43QNMROO24XE4D7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K/action/storage_attestation","attest_author":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K/action/author_attestation","sign_citation":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K/action/citation_signature","submit_replication":"https://pith.science/pith/DZ6D2OVFZZ43QNMROO24XE4D7K/action/replication_record"}},"created_at":"2026-07-05T00:02:40.093320+00:00","updated_at":"2026-07-05T00:02:40.093320+00:00"}