{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:6K5RJZGSHRJGWO3MRAYMQRLLIL","short_pith_number":"pith:6K5RJZGS","schema_version":"1.0","canonical_sha256":"f2bb14e4d23c526b3b6c8830c8456b42f4d2c44327e285b5874461f0b1eb7f61","source":{"kind":"arxiv","id":"1805.04612","version":1},"attestation_state":"computed","paper":{"title":"Twitter User Geolocation using Deep Multiview Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Bruno Cornelis, Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis, Tien Huu Do","submitted_at":"2018-05-11T22:47:53Z","abstract_excerpt":"Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the la"},"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":"1805.04612","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-05-11T22:47:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"de7292674cf51a8ea22363e8c16c2edcd05ed8f6421170a531a3412eba162e4c","abstract_canon_sha256":"ba05f71930edd300b09422152b3ba462651380d8c3eedb7990a6d79977616a2f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:06.908786Z","signature_b64":"ebAuWiohDGiew5mqYNDQ5F6HEFJaU4e/pgOLgaGEo/vL7q2Nf0wyaiSW27sh8/nHFRhJlz+PssHf/wDCQeVgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2bb14e4d23c526b3b6c8830c8456b42f4d2c44327e285b5874461f0b1eb7f61","last_reissued_at":"2026-05-18T00:16:06.908141Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:06.908141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Twitter User Geolocation using Deep Multiview Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Bruno Cornelis, Duc Minh Nguyen, Evaggelia Tsiligianni, Nikos Deligiannis, Tien Huu Do","submitted_at":"2018-05-11T22:47:53Z","abstract_excerpt":"Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model. Our approach, named Multi-Entry Neural Network (MENET), leverages the la"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.04612","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":"1805.04612","created_at":"2026-05-18T00:16:06.908242+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.04612v1","created_at":"2026-05-18T00:16:06.908242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.04612","created_at":"2026-05-18T00:16:06.908242+00:00"},{"alias_kind":"pith_short_12","alias_value":"6K5RJZGSHRJG","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"6K5RJZGSHRJGWO3M","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"6K5RJZGS","created_at":"2026-05-18T12:32:08.215937+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/6K5RJZGSHRJGWO3MRAYMQRLLIL","json":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL.json","graph_json":"https://pith.science/api/pith-number/6K5RJZGSHRJGWO3MRAYMQRLLIL/graph.json","events_json":"https://pith.science/api/pith-number/6K5RJZGSHRJGWO3MRAYMQRLLIL/events.json","paper":"https://pith.science/paper/6K5RJZGS"},"agent_actions":{"view_html":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL","download_json":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL.json","view_paper":"https://pith.science/paper/6K5RJZGS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.04612&json=true","fetch_graph":"https://pith.science/api/pith-number/6K5RJZGSHRJGWO3MRAYMQRLLIL/graph.json","fetch_events":"https://pith.science/api/pith-number/6K5RJZGSHRJGWO3MRAYMQRLLIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL/action/storage_attestation","attest_author":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL/action/author_attestation","sign_citation":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL/action/citation_signature","submit_replication":"https://pith.science/pith/6K5RJZGSHRJGWO3MRAYMQRLLIL/action/replication_record"}},"created_at":"2026-05-18T00:16:06.908242+00:00","updated_at":"2026-05-18T00:16:06.908242+00:00"}