{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:67TZMGWBGYGJCIJP53NLDPGWZE","short_pith_number":"pith:67TZMGWB","canonical_record":{"source":{"id":"1902.03111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-03T21:36:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3edbd91aa5d9b5a14dc42abf652dd44bed6a80f56a42124d3b1cbff36d93bc9e","abstract_canon_sha256":"8305216815cf4560b3c47b5d54930960e12235b3b07cfdbb9df94905c18c0332"},"schema_version":"1.0"},"canonical_sha256":"f7e7961ac1360c91212feedab1bcd6c90ecb4ec9bfc4c8f8390771b82ee642c5","source":{"kind":"arxiv","id":"1902.03111","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03111","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03111v2","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03111","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"pith_short_12","alias_value":"67TZMGWBGYGJ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"67TZMGWBGYGJCIJP","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"67TZMGWB","created_at":"2026-05-18T12:33:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:67TZMGWBGYGJCIJP53NLDPGWZE","target":"record","payload":{"canonical_record":{"source":{"id":"1902.03111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-03T21:36:23Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3edbd91aa5d9b5a14dc42abf652dd44bed6a80f56a42124d3b1cbff36d93bc9e","abstract_canon_sha256":"8305216815cf4560b3c47b5d54930960e12235b3b07cfdbb9df94905c18c0332"},"schema_version":"1.0"},"canonical_sha256":"f7e7961ac1360c91212feedab1bcd6c90ecb4ec9bfc4c8f8390771b82ee642c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:21.852192Z","signature_b64":"yGiVtxvoiOiN60gxPV6+yroHPGObmNdvTe29oujXYmesRlwGuQ+pesvLMjzCER+60IW3wtvED00VmWiblJqaDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7e7961ac1360c91212feedab1bcd6c90ecb4ec9bfc4c8f8390771b82ee642c5","last_reissued_at":"2026-05-17T23:41:21.851420Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:21.851420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.03111","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yfzLbLsK7HpacICwUYgmlzgnvZNNjYe7AGEJk1G5jx/Sqk34qJKV8nW5XwN5VZl32WdZcCyPIWoPEUwYmIlLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:15:42.565711Z"},"content_sha256":"cc9275380d062fd51e67ca6424894e4a327b83824f6717c9f82f49dabf08231e","schema_version":"1.0","event_id":"sha256:cc9275380d062fd51e67ca6424894e4a327b83824f6717c9f82f49dabf08231e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:67TZMGWBGYGJCIJP53NLDPGWZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"High-resolution home location prediction from tweets using deep learning with dynamic structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.SI","authors_text":"Ashok Srinivasan, Meysam Ghaffari, Xiuwen Liu","submitted_at":"2019-02-03T21:36:23Z","abstract_excerpt":"Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03111","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:41:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oaYQEA69OgCo4TninM+KysR2ABZ6s0UmPCWnHMj65kSbx+s8J0ncCNWDW5lwYxU3Rd3N27KKaXwdvWMzUNkaAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T00:15:42.566469Z"},"content_sha256":"9e483ae63cd22ce9215c766f89c413d573d8a884b148fad29b618704183d6160","schema_version":"1.0","event_id":"sha256:9e483ae63cd22ce9215c766f89c413d573d8a884b148fad29b618704183d6160"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/67TZMGWBGYGJCIJP53NLDPGWZE/bundle.json","state_url":"https://pith.science/pith/67TZMGWBGYGJCIJP53NLDPGWZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/67TZMGWBGYGJCIJP53NLDPGWZE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-31T00:15:42Z","links":{"resolver":"https://pith.science/pith/67TZMGWBGYGJCIJP53NLDPGWZE","bundle":"https://pith.science/pith/67TZMGWBGYGJCIJP53NLDPGWZE/bundle.json","state":"https://pith.science/pith/67TZMGWBGYGJCIJP53NLDPGWZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/67TZMGWBGYGJCIJP53NLDPGWZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:67TZMGWBGYGJCIJP53NLDPGWZE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8305216815cf4560b3c47b5d54930960e12235b3b07cfdbb9df94905c18c0332","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-03T21:36:23Z","title_canon_sha256":"3edbd91aa5d9b5a14dc42abf652dd44bed6a80f56a42124d3b1cbff36d93bc9e"},"schema_version":"1.0","source":{"id":"1902.03111","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03111","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03111v2","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03111","created_at":"2026-05-17T23:41:21Z"},{"alias_kind":"pith_short_12","alias_value":"67TZMGWBGYGJ","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_16","alias_value":"67TZMGWBGYGJCIJP","created_at":"2026-05-18T12:33:10Z"},{"alias_kind":"pith_short_8","alias_value":"67TZMGWB","created_at":"2026-05-18T12:33:10Z"}],"graph_snapshots":[{"event_id":"sha256:9e483ae63cd22ce9215c766f89c413d573d8a884b148fad29b618704183d6160","target":"graph","created_at":"2026-05-17T23:41:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Con","authors_text":"Ashok Srinivasan, Meysam Ghaffari, Xiuwen Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-03T21:36:23Z","title":"High-resolution home location prediction from tweets using deep learning with dynamic structure"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03111","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cc9275380d062fd51e67ca6424894e4a327b83824f6717c9f82f49dabf08231e","target":"record","created_at":"2026-05-17T23:41:21Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8305216815cf4560b3c47b5d54930960e12235b3b07cfdbb9df94905c18c0332","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-02-03T21:36:23Z","title_canon_sha256":"3edbd91aa5d9b5a14dc42abf652dd44bed6a80f56a42124d3b1cbff36d93bc9e"},"schema_version":"1.0","source":{"id":"1902.03111","kind":"arxiv","version":2}},"canonical_sha256":"f7e7961ac1360c91212feedab1bcd6c90ecb4ec9bfc4c8f8390771b82ee642c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7e7961ac1360c91212feedab1bcd6c90ecb4ec9bfc4c8f8390771b82ee642c5","first_computed_at":"2026-05-17T23:41:21.851420Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:21.851420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yGiVtxvoiOiN60gxPV6+yroHPGObmNdvTe29oujXYmesRlwGuQ+pesvLMjzCER+60IW3wtvED00VmWiblJqaDg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:21.852192Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.03111","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc9275380d062fd51e67ca6424894e4a327b83824f6717c9f82f49dabf08231e","sha256:9e483ae63cd22ce9215c766f89c413d573d8a884b148fad29b618704183d6160"],"state_sha256":"7bf2d098c67f15985ffe5fc97114b6c8c4a76a6fd55d60fd96669e1dbcf9b9fe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9xh7InzsmJwlvwr7U8GC6rBv5658/fWYE8z29xJZw0JPjJuWF3KODWKI0aHurFfTtiPCekmOrD1ZBdRPDamGDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T00:15:42.569720Z","bundle_sha256":"43700e366b87d0852990d96cc9a2975cc3a1c6402b4bcbeffd0ec25f9674dc20"}}