{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CKXZQNN26YWMG6LBKBY4LNLT3V","short_pith_number":"pith:CKXZQNN2","canonical_record":{"source":{"id":"2205.01603","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T16:32:09Z","cross_cats_sorted":[],"title_canon_sha256":"c23cf5d712c616502403ddef11ff06aec487179d2805d820ceefb40bd8bfc4d9","abstract_canon_sha256":"a01f169e9248577a0a8fe767f57414a0c9e99ff91e01f6584406821272ac5542"},"schema_version":"1.0"},"canonical_sha256":"12af9835baf62cc379615071c5b573dd40eac9975ecc89b627980e7b4371ad52","source":{"kind":"arxiv","id":"2205.01603","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01603","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01603v1","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01603","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_12","alias_value":"CKXZQNN26YWM","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_16","alias_value":"CKXZQNN26YWMG6LB","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_8","alias_value":"CKXZQNN2","created_at":"2026-07-05T04:20:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CKXZQNN26YWMG6LBKBY4LNLT3V","target":"record","payload":{"canonical_record":{"source":{"id":"2205.01603","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T16:32:09Z","cross_cats_sorted":[],"title_canon_sha256":"c23cf5d712c616502403ddef11ff06aec487179d2805d820ceefb40bd8bfc4d9","abstract_canon_sha256":"a01f169e9248577a0a8fe767f57414a0c9e99ff91e01f6584406821272ac5542"},"schema_version":"1.0"},"canonical_sha256":"12af9835baf62cc379615071c5b573dd40eac9975ecc89b627980e7b4371ad52","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:06.753676Z","signature_b64":"72HwO2RQCyzbUDSdW5Ju3RCxR75yA4hBtGJSyGADD0N2oFu6dtnOXeiZX0c0/TtTbp/SK7muPuL1Smw6q05tDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12af9835baf62cc379615071c5b573dd40eac9975ecc89b627980e7b4371ad52","last_reissued_at":"2026-07-05T04:20:06.753165Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:06.753165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.01603","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:20:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"x5ptQvr9Q0D4FGtpFB/mrbUtFxhrGQRgwB7ykw5r1E1sAH6nbqEW8AotjTXETgZBHMdd4oz3q6FRS7vMMJt2DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:05.064475Z"},"content_sha256":"3ac1774be9fc9fa586715705269a00310faf20e07745fac2e4724d2934d6c6ce","schema_version":"1.0","event_id":"sha256:3ac1774be9fc9fa586715705269a00310faf20e07745fac2e4724d2934d6c6ce"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CKXZQNN26YWMG6LBKBY4LNLT3V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aria Haghighi, Kenny Leung, Vivek Kulkarni","submitted_at":"2022-05-03T16:32:09Z","abstract_excerpt":"Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classification of such posts is quite challenging because of (a) a large topic space (b) short text with weak topical cues, and (c) multiple topic associations per post. In contrast to most prior work which only focuses on post classification into a small number of topics ($10$-$20$), we consider the task of large-scale topic classification in the context of Twitter where the topic space is $10$ times larger with potentiall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01603","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.01603/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:20:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DtLAL74PEf3n2ql9O4p2ZWmu4KW18QFtfdQo0tTnzSYbi2oKyjp/kJ2Ugbb/YBYdsh5jaLrXfwqXfpAOBIu+BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T07:22:05.065328Z"},"content_sha256":"d1264f7bab1e794d7d47f29d1a0c1a553c71ad7dc368a266a5ceff43e7847901","schema_version":"1.0","event_id":"sha256:d1264f7bab1e794d7d47f29d1a0c1a553c71ad7dc368a266a5ceff43e7847901"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/bundle.json","state_url":"https://pith.science/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/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":"2026-08-04T07:22:05Z","links":{"resolver":"https://pith.science/pith/CKXZQNN26YWMG6LBKBY4LNLT3V","bundle":"https://pith.science/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/bundle.json","state":"https://pith.science/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CKXZQNN26YWMG6LBKBY4LNLT3V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CKXZQNN26YWMG6LBKBY4LNLT3V","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":"a01f169e9248577a0a8fe767f57414a0c9e99ff91e01f6584406821272ac5542","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T16:32:09Z","title_canon_sha256":"c23cf5d712c616502403ddef11ff06aec487179d2805d820ceefb40bd8bfc4d9"},"schema_version":"1.0","source":{"id":"2205.01603","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01603","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01603v1","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01603","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_12","alias_value":"CKXZQNN26YWM","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_16","alias_value":"CKXZQNN26YWMG6LB","created_at":"2026-07-05T04:20:06Z"},{"alias_kind":"pith_short_8","alias_value":"CKXZQNN2","created_at":"2026-07-05T04:20:06Z"}],"graph_snapshots":[{"event_id":"sha256:d1264f7bab1e794d7d47f29d1a0c1a553c71ad7dc368a266a5ceff43e7847901","target":"graph","created_at":"2026-07-05T04:20:06Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.01603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classification of such posts is quite challenging because of (a) a large topic space (b) short text with weak topical cues, and (c) multiple topic associations per post. In contrast to most prior work which only focuses on post classification into a small number of topics ($10$-$20$), we consider the task of large-scale topic classification in the context of Twitter where the topic space is $10$ times larger with potentiall","authors_text":"Aria Haghighi, Kenny Leung, Vivek Kulkarni","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T16:32:09Z","title":"CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01603","kind":"arxiv","version":1},"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":"sha256:3ac1774be9fc9fa586715705269a00310faf20e07745fac2e4724d2934d6c6ce","target":"record","created_at":"2026-07-05T04:20:06Z","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":"a01f169e9248577a0a8fe767f57414a0c9e99ff91e01f6584406821272ac5542","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-03T16:32:09Z","title_canon_sha256":"c23cf5d712c616502403ddef11ff06aec487179d2805d820ceefb40bd8bfc4d9"},"schema_version":"1.0","source":{"id":"2205.01603","kind":"arxiv","version":1}},"canonical_sha256":"12af9835baf62cc379615071c5b573dd40eac9975ecc89b627980e7b4371ad52","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12af9835baf62cc379615071c5b573dd40eac9975ecc89b627980e7b4371ad52","first_computed_at":"2026-07-05T04:20:06.753165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:20:06.753165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"72HwO2RQCyzbUDSdW5Ju3RCxR75yA4hBtGJSyGADD0N2oFu6dtnOXeiZX0c0/TtTbp/SK7muPuL1Smw6q05tDw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:20:06.753676Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.01603","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ac1774be9fc9fa586715705269a00310faf20e07745fac2e4724d2934d6c6ce","sha256:d1264f7bab1e794d7d47f29d1a0c1a553c71ad7dc368a266a5ceff43e7847901"],"state_sha256":"01c7918e0deb4022ba668e3633687f28e1543d5fe05baf6dc57577a75f127eee"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HQlk/6LllPlMGUCB3Rg3cO6mKBx4zPkUWVUakjyvp02pUdISgK1T0s0JjYOXj6lcOWGZyMM6AjwJPOyf79KCCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T07:22:05.076602Z","bundle_sha256":"4fd2185787522a12fd76de4abe1af2e59aba9d2873b3daa683315abafec6ec2e"}}