{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZQRQSCN5PXLQRJEPCSQ3EUSIBW","short_pith_number":"pith:ZQRQSCN5","canonical_record":{"source":{"id":"1704.00898","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-04T07:24:15Z","cross_cats_sorted":[],"title_canon_sha256":"886cfaf7cae536e02deec43719784b58a7918d6dcb22c4483522d00028218ba9","abstract_canon_sha256":"7eca502b2cceb389432a468cbffe13b63435a7ccf4eb2eaf439dc562b205c2ac"},"schema_version":"1.0"},"canonical_sha256":"cc230909bd7dd708a48f14a1b252480d999f80ac6da9bc1dbbddcfc50600a2b2","source":{"kind":"arxiv","id":"1704.00898","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.00898","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"arxiv_version","alias_value":"1704.00898v2","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00898","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"pith_short_12","alias_value":"ZQRQSCN5PXLQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZQRQSCN5PXLQRJEP","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZQRQSCN5","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZQRQSCN5PXLQRJEPCSQ3EUSIBW","target":"record","payload":{"canonical_record":{"source":{"id":"1704.00898","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-04T07:24:15Z","cross_cats_sorted":[],"title_canon_sha256":"886cfaf7cae536e02deec43719784b58a7918d6dcb22c4483522d00028218ba9","abstract_canon_sha256":"7eca502b2cceb389432a468cbffe13b63435a7ccf4eb2eaf439dc562b205c2ac"},"schema_version":"1.0"},"canonical_sha256":"cc230909bd7dd708a48f14a1b252480d999f80ac6da9bc1dbbddcfc50600a2b2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:57.769658Z","signature_b64":"2V7zQIwJJGWuJKF/0w0sFrJNahvq/pTnwxTy5uduQDe1wRIk3lyJzcVwBBjSBy8vHoJt+DGKAR1DsgGTG8pTDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc230909bd7dd708a48f14a1b252480d999f80ac6da9bc1dbbddcfc50600a2b2","last_reissued_at":"2026-05-18T00:41:57.769141Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:57.769141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1704.00898","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":"2026-05-18T00:41:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IDKqZ9PpaqAbX1TDQLzEbWFLVTBsRK2My5nIhNr62VG4ktYwf/TKl8h1nOoEIeXyWBhbupDyZv7PF2X1t3OJAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:27:51.119983Z"},"content_sha256":"dcae230cc9d9ed29b6a080536ee3a5ab6959f6de151b2276dd9334c971074cef","schema_version":"1.0","event_id":"sha256:dcae230cc9d9ed29b6a080536ee3a5ab6959f6de151b2276dd9334c971074cef"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZQRQSCN5PXLQRJEPCSQ3EUSIBW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpretation of Semantic Tweet Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"J Ganesh, Manish Gupta, Vasudeva Varma","submitted_at":"2017-04-04T07:24:15Z","abstract_excerpt":"Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00898","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":"2026-05-18T00:41:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P8TbGb0xxqDiapBRCpJLDPoEJHhDDjsN7oKsrGUpH5tgfWaIdZebPKLM7HTRYSvEaEg+r0fN7Grvq4DaMoVHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:27:51.120349Z"},"content_sha256":"65989f4d47546b93449a553a3c54e5117278421115baf4d88b758ffb3a0b183d","schema_version":"1.0","event_id":"sha256:65989f4d47546b93449a553a3c54e5117278421115baf4d88b758ffb3a0b183d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/bundle.json","state_url":"https://pith.science/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/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":"2026-05-21T14:27:51Z","links":{"resolver":"https://pith.science/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW","bundle":"https://pith.science/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/bundle.json","state":"https://pith.science/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZQRQSCN5PXLQRJEPCSQ3EUSIBW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZQRQSCN5PXLQRJEPCSQ3EUSIBW","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":"7eca502b2cceb389432a468cbffe13b63435a7ccf4eb2eaf439dc562b205c2ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-04T07:24:15Z","title_canon_sha256":"886cfaf7cae536e02deec43719784b58a7918d6dcb22c4483522d00028218ba9"},"schema_version":"1.0","source":{"id":"1704.00898","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1704.00898","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"arxiv_version","alias_value":"1704.00898v2","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.00898","created_at":"2026-05-18T00:41:57Z"},{"alias_kind":"pith_short_12","alias_value":"ZQRQSCN5PXLQ","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZQRQSCN5PXLQRJEP","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZQRQSCN5","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:65989f4d47546b93449a553a3c54e5117278421115baf4d88b758ffb3a0b183d","target":"graph","created_at":"2026-05-18T00:41:57Z","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":"Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in o","authors_text":"J Ganesh, Manish Gupta, Vasudeva Varma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-04T07:24:15Z","title":"Interpretation of Semantic Tweet Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.00898","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:dcae230cc9d9ed29b6a080536ee3a5ab6959f6de151b2276dd9334c971074cef","target":"record","created_at":"2026-05-18T00:41:57Z","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":"7eca502b2cceb389432a468cbffe13b63435a7ccf4eb2eaf439dc562b205c2ac","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-04-04T07:24:15Z","title_canon_sha256":"886cfaf7cae536e02deec43719784b58a7918d6dcb22c4483522d00028218ba9"},"schema_version":"1.0","source":{"id":"1704.00898","kind":"arxiv","version":2}},"canonical_sha256":"cc230909bd7dd708a48f14a1b252480d999f80ac6da9bc1dbbddcfc50600a2b2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cc230909bd7dd708a48f14a1b252480d999f80ac6da9bc1dbbddcfc50600a2b2","first_computed_at":"2026-05-18T00:41:57.769141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:57.769141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2V7zQIwJJGWuJKF/0w0sFrJNahvq/pTnwxTy5uduQDe1wRIk3lyJzcVwBBjSBy8vHoJt+DGKAR1DsgGTG8pTDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:57.769658Z","signed_message":"canonical_sha256_bytes"},"source_id":"1704.00898","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dcae230cc9d9ed29b6a080536ee3a5ab6959f6de151b2276dd9334c971074cef","sha256:65989f4d47546b93449a553a3c54e5117278421115baf4d88b758ffb3a0b183d"],"state_sha256":"f3573c28ba7501d9c1ea3c710feb95285e3f338520ad78241a38cd661a93a4a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RVyxmPt2FXnQUXR9XtRm4JUvvihUVJwvXdg155WHQBu6XMAinaf8AeneeQ/XzSmKMjdtg0XuA3/zyQhqDgSeBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T14:27:51.122406Z","bundle_sha256":"e2af329c574762f2783328553dd8798f53724590ece148bc441b539883be65dd"}}