{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:VVFLYU7TBXXPWRCE4HOTSCKLOU","short_pith_number":"pith:VVFLYU7T","canonical_record":{"source":{"id":"2403.12079","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-01T17:14:41Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"2e351aa1b5cdba6a9c949791a7376e4d2c1de18afcd9f69ff659f3bfec6f69f6","abstract_canon_sha256":"32c3b227e9b209de53b09f84282905ef6cdb9f74d730f3b687e401b09131918b"},"schema_version":"1.0"},"canonical_sha256":"ad4abc53f30deefb4444e1dd39094b75203f480981662b542deb5faeb07c97f0","source":{"kind":"arxiv","id":"2403.12079","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.12079","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"2403.12079v1","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12079","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"VVFLYU7TBXXP","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_16","alias_value":"VVFLYU7TBXXPWRCE","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_8","alias_value":"VVFLYU7T","created_at":"2026-07-05T07:57:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:VVFLYU7TBXXPWRCE4HOTSCKLOU","target":"record","payload":{"canonical_record":{"source":{"id":"2403.12079","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-01T17:14:41Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"2e351aa1b5cdba6a9c949791a7376e4d2c1de18afcd9f69ff659f3bfec6f69f6","abstract_canon_sha256":"32c3b227e9b209de53b09f84282905ef6cdb9f74d730f3b687e401b09131918b"},"schema_version":"1.0"},"canonical_sha256":"ad4abc53f30deefb4444e1dd39094b75203f480981662b542deb5faeb07c97f0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:55.006001Z","signature_b64":"xbTpHwtVTno6zN0NQ1nlguACu8EjKy5ujYUAHeDCkJQTnEJotXcdy+oKjTkESP5FCmdAq7TuO/loPra8JECGCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ad4abc53f30deefb4444e1dd39094b75203f480981662b542deb5faeb07c97f0","last_reissued_at":"2026-07-05T07:57:55.005429Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:55.005429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.12079","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":"2026-07-05T07:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HXeAlnbAr671CI+MWFWXw8IGUUckFqhKXQTNiPqTd9yWBpcARKjnsppPSL8XJkeRdBo/N1NqGl4AAMklcDXZAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T21:58:24.686067Z"},"content_sha256":"3e960366ab9f12bc4303d3b920d0af80ecd4b4182ebf9aeaf5504d134b1c892b","schema_version":"1.0","event_id":"sha256:3e960366ab9f12bc4303d3b920d0af80ecd4b4182ebf9aeaf5504d134b1c892b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:VVFLYU7TBXXPWRCE4HOTSCKLOU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Beats: A Recipe to Song Popularity? A machine learning approach","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"cs.IR","authors_text":"Florian Mayer, Jung, Niklas Sebastian","submitted_at":"2024-03-01T17:14:41Z","abstract_excerpt":"Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12079","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/2403.12079/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":"2026-07-05T07:57:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Eye3uokDpteWSOeCWn90wwvK6+4WrsZBFFJ20RvHVZXicbWZGL6D32v0O3XrdvxRQkbzbbjcEqQveEaMHGA1Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T21:58:24.686711Z"},"content_sha256":"8fe56558a0c91c54e51055695bb6b2974227864f0604cbcb8ee5438bc0d3301b","schema_version":"1.0","event_id":"sha256:8fe56558a0c91c54e51055695bb6b2974227864f0604cbcb8ee5438bc0d3301b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/bundle.json","state_url":"https://pith.science/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/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":"2026-07-07T21:58:24Z","links":{"resolver":"https://pith.science/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU","bundle":"https://pith.science/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/bundle.json","state":"https://pith.science/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VVFLYU7TBXXPWRCE4HOTSCKLOU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:VVFLYU7TBXXPWRCE4HOTSCKLOU","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":"32c3b227e9b209de53b09f84282905ef6cdb9f74d730f3b687e401b09131918b","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-01T17:14:41Z","title_canon_sha256":"2e351aa1b5cdba6a9c949791a7376e4d2c1de18afcd9f69ff659f3bfec6f69f6"},"schema_version":"1.0","source":{"id":"2403.12079","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.12079","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"arxiv_version","alias_value":"2403.12079v1","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12079","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_12","alias_value":"VVFLYU7TBXXP","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_16","alias_value":"VVFLYU7TBXXPWRCE","created_at":"2026-07-05T07:57:55Z"},{"alias_kind":"pith_short_8","alias_value":"VVFLYU7T","created_at":"2026-07-05T07:57:55Z"}],"graph_snapshots":[{"event_id":"sha256:8fe56558a0c91c54e51055695bb6b2974227864f0604cbcb8ee5438bc0d3301b","target":"graph","created_at":"2026-07-05T07:57:55Z","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/2403.12079/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics and their impact on popularity. Results: Ordinary Least","authors_text":"Florian Mayer, Jung, Niklas Sebastian","cross_cats":["cs.LG","stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-01T17:14:41Z","title":"Beyond Beats: A Recipe to Song Popularity? A machine learning approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12079","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":"sha256:3e960366ab9f12bc4303d3b920d0af80ecd4b4182ebf9aeaf5504d134b1c892b","target":"record","created_at":"2026-07-05T07:57:55Z","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":"32c3b227e9b209de53b09f84282905ef6cdb9f74d730f3b687e401b09131918b","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-01T17:14:41Z","title_canon_sha256":"2e351aa1b5cdba6a9c949791a7376e4d2c1de18afcd9f69ff659f3bfec6f69f6"},"schema_version":"1.0","source":{"id":"2403.12079","kind":"arxiv","version":1}},"canonical_sha256":"ad4abc53f30deefb4444e1dd39094b75203f480981662b542deb5faeb07c97f0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad4abc53f30deefb4444e1dd39094b75203f480981662b542deb5faeb07c97f0","first_computed_at":"2026-07-05T07:57:55.005429Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:57:55.005429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xbTpHwtVTno6zN0NQ1nlguACu8EjKy5ujYUAHeDCkJQTnEJotXcdy+oKjTkESP5FCmdAq7TuO/loPra8JECGCw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:57:55.006001Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.12079","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e960366ab9f12bc4303d3b920d0af80ecd4b4182ebf9aeaf5504d134b1c892b","sha256:8fe56558a0c91c54e51055695bb6b2974227864f0604cbcb8ee5438bc0d3301b"],"state_sha256":"f0e8976a44ee96d6c6b8776a16a384c96ca91aa6b69163899c35bcb51d9dbb6c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0kyo1354SOoBp6+Ryd929wY8vvra0UF/fnUfyqlnMCfKRQgs8PVhNh0MuNQo8N4BOc7ETc0SEqh2mDIibzivDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T21:58:24.690334Z","bundle_sha256":"147e971b01393e620ef69f03960214eaeffeac4be335c764e5c6336d1d3da3f7"}}