{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ANFMTAWSC6VGAQ2CM3SJRXHAWW","short_pith_number":"pith:ANFMTAWS","canonical_record":{"source":{"id":"1603.09436","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-03-31T01:52:34Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"ef9a1b0de8c63bdd7e3d1900c46ff014ada348cee3188c2e33ee196496a8fa47","abstract_canon_sha256":"5ab5510b4d13839c243338946ea04d3825468caa8ce37bf80d8d2f62ca6d2926"},"schema_version":"1.0"},"canonical_sha256":"034ac982d217aa60434266e498dce0b58de630ec514515e221738adc6a826bd1","source":{"kind":"arxiv","id":"1603.09436","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.09436","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"arxiv_version","alias_value":"1603.09436v1","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.09436","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"pith_short_12","alias_value":"ANFMTAWSC6VG","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"ANFMTAWSC6VGAQ2C","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"ANFMTAWS","created_at":"2026-05-18T12:30:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ANFMTAWSC6VGAQ2CM3SJRXHAWW","target":"record","payload":{"canonical_record":{"source":{"id":"1603.09436","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-03-31T01:52:34Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"ef9a1b0de8c63bdd7e3d1900c46ff014ada348cee3188c2e33ee196496a8fa47","abstract_canon_sha256":"5ab5510b4d13839c243338946ea04d3825468caa8ce37bf80d8d2f62ca6d2926"},"schema_version":"1.0"},"canonical_sha256":"034ac982d217aa60434266e498dce0b58de630ec514515e221738adc6a826bd1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:58.627951Z","signature_b64":"y1wHfpXnYL3+gdHT8KUtxmqp2O074pJBiTnmFAl7yXrIrabIYE2evBbtg3sLx1QoVQiV4IqmNZhJJcO+jMgdAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"034ac982d217aa60434266e498dce0b58de630ec514515e221738adc6a826bd1","last_reissued_at":"2026-05-18T01:17:58.627178Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:58.627178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.09436","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":"2026-05-18T01:17:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T+lEMRRJlQKyWaYO3+d7Dok2VqJ6DW4aZF1Fh9NWo2UX7waH11NzulAslKLIlK9IkT7lEWiuNO5hvD/qfafdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T06:39:19.219400Z"},"content_sha256":"72dbb520f0f590ee628becb89fb204a1aea350eb819f3b38db734df18b4ebe68","schema_version":"1.0","event_id":"sha256:72dbb520f0f590ee628becb89fb204a1aea350eb819f3b38db734df18b4ebe68"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ANFMTAWSC6VGAQ2CM3SJRXHAWW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predictability of Popularity: Gaps between Prediction and Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"cs.SI","authors_text":"Amit Sharma, Benjamin Shulman, Dan Cosley","submitted_at":"2016-03-31T01:52:34Z","abstract_excerpt":"Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item's popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.09436","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:17:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wWmGYI892Jo50HDDQFA/z/6+kDQnkVej6YUKl0odHic9vdtuBvb34bcF7bgsNeo38j2vlZbi37YzOSo8Z8M5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T06:39:19.220051Z"},"content_sha256":"ec691fcb48c098969650adc99ddb0603b443e9e433524771978c1ce908732285","schema_version":"1.0","event_id":"sha256:ec691fcb48c098969650adc99ddb0603b443e9e433524771978c1ce908732285"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/bundle.json","state_url":"https://pith.science/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/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":"2026-05-25T06:39:19Z","links":{"resolver":"https://pith.science/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW","bundle":"https://pith.science/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/bundle.json","state":"https://pith.science/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ANFMTAWSC6VGAQ2CM3SJRXHAWW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ANFMTAWSC6VGAQ2CM3SJRXHAWW","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":"5ab5510b4d13839c243338946ea04d3825468caa8ce37bf80d8d2f62ca6d2926","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-03-31T01:52:34Z","title_canon_sha256":"ef9a1b0de8c63bdd7e3d1900c46ff014ada348cee3188c2e33ee196496a8fa47"},"schema_version":"1.0","source":{"id":"1603.09436","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.09436","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"arxiv_version","alias_value":"1603.09436v1","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.09436","created_at":"2026-05-18T01:17:58Z"},{"alias_kind":"pith_short_12","alias_value":"ANFMTAWSC6VG","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_16","alias_value":"ANFMTAWSC6VGAQ2C","created_at":"2026-05-18T12:30:07Z"},{"alias_kind":"pith_short_8","alias_value":"ANFMTAWS","created_at":"2026-05-18T12:30:07Z"}],"graph_snapshots":[{"event_id":"sha256:ec691fcb48c098969650adc99ddb0603b443e9e433524771978c1ce908732285","target":"graph","created_at":"2026-05-18T01:17:58Z","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":"Can we predict the future popularity of a song, movie or tweet? Recent work suggests that although it may be hard to predict an item's popularity when it is first introduced, peeking into its early adopters and properties of their social network makes the problem easier. We test the robustness of such claims by using data from social networks spanning music, books, photos, and URLs. We find a stronger result: not only do predictive models with peeking achieve high accuracy on all datasets, they also generalize well, so much so that models trained on any one dataset perform with comparable accu","authors_text":"Amit Sharma, Benjamin Shulman, Dan Cosley","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-03-31T01:52:34Z","title":"Predictability of Popularity: Gaps between Prediction and Understanding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.09436","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":"sha256:72dbb520f0f590ee628becb89fb204a1aea350eb819f3b38db734df18b4ebe68","target":"record","created_at":"2026-05-18T01:17:58Z","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":"5ab5510b4d13839c243338946ea04d3825468caa8ce37bf80d8d2f62ca6d2926","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2016-03-31T01:52:34Z","title_canon_sha256":"ef9a1b0de8c63bdd7e3d1900c46ff014ada348cee3188c2e33ee196496a8fa47"},"schema_version":"1.0","source":{"id":"1603.09436","kind":"arxiv","version":1}},"canonical_sha256":"034ac982d217aa60434266e498dce0b58de630ec514515e221738adc6a826bd1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"034ac982d217aa60434266e498dce0b58de630ec514515e221738adc6a826bd1","first_computed_at":"2026-05-18T01:17:58.627178Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:58.627178Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y1wHfpXnYL3+gdHT8KUtxmqp2O074pJBiTnmFAl7yXrIrabIYE2evBbtg3sLx1QoVQiV4IqmNZhJJcO+jMgdAA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:58.627951Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.09436","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72dbb520f0f590ee628becb89fb204a1aea350eb819f3b38db734df18b4ebe68","sha256:ec691fcb48c098969650adc99ddb0603b443e9e433524771978c1ce908732285"],"state_sha256":"2ef939f3c0889ac1d8a3d35b803f6b6f25f103d524cb39cfdc1d8532b9977eb3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jag/CI+FqS7b96Nf4is6Divtc1JNx5yNYgZbo1aNlRFtwqQrMRwkCxiA2pgu+DPJKF3VFpJY2ybJL3gu1ppXBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T06:39:19.223467Z","bundle_sha256":"7e864cfceae7a69ec2fdb47d1150ef14d76675978fe19f86e051edebf828c06f"}}