{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TFWLEHT3I4RYWTXQMJONR74DEZ","short_pith_number":"pith:TFWLEHT3","schema_version":"1.0","canonical_sha256":"996cb21e7b47238b4ef0625cd8ff83267b8c7ea8cfe1c57b857a1332de8b5365","source":{"kind":"arxiv","id":"2407.07227","version":1},"attestation_state":"computed","paper":{"title":"Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.SI","authors_text":"Brian Ekdale, Hussam Habib, Raven Maragh-Lloyd, Rishab Nithyanand, Ryan Stoldt","submitted_at":"2024-07-09T20:47:34Z","abstract_excerpt":"Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experiences and boosts engagement. Among various modalities through which this algorithmically curated content may be delivered, the homepage feed is the most prominent. This paper presents a comprehensive study of how prior user interactions influence the content presented on users' homepage feeds across three major platforms: YouTube, Reddit, and X (for"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2407.07227","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.SI","submitted_at":"2024-07-09T20:47:34Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"c441e11afe1c540e7969c64c2fd7eaef4e19b9a12d0b66dcc8b10b54d0916056","abstract_canon_sha256":"98dbbe88e7ca075f02e826c21e51211e98aae82ed00f537c4a6779539163e6ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:08.514054Z","signature_b64":"YGHtWWJxsnz3nWynWxweFT9QEQ9e22BQ1M41E7lyEM/cl36jpwMocIPwKhtxUBeZmzgbGyIJgOAix2MI19IAAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"996cb21e7b47238b4ef0625cd8ff83267b8c7ea8cfe1c57b857a1332de8b5365","last_reissued_at":"2026-07-05T08:42:08.513673Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:08.513673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.SI","authors_text":"Brian Ekdale, Hussam Habib, Raven Maragh-Lloyd, Rishab Nithyanand, Ryan Stoldt","submitted_at":"2024-07-09T20:47:34Z","abstract_excerpt":"Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experiences and boosts engagement. Among various modalities through which this algorithmically curated content may be delivered, the homepage feed is the most prominent. This paper presents a comprehensive study of how prior user interactions influence the content presented on users' homepage feeds across three major platforms: YouTube, Reddit, and X (for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.07227","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/2407.07227/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2407.07227","created_at":"2026-07-05T08:42:08.513740+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.07227v1","created_at":"2026-07-05T08:42:08.513740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.07227","created_at":"2026-07-05T08:42:08.513740+00:00"},{"alias_kind":"pith_short_12","alias_value":"TFWLEHT3I4RY","created_at":"2026-07-05T08:42:08.513740+00:00"},{"alias_kind":"pith_short_16","alias_value":"TFWLEHT3I4RYWTXQ","created_at":"2026-07-05T08:42:08.513740+00:00"},{"alias_kind":"pith_short_8","alias_value":"TFWLEHT3","created_at":"2026-07-05T08:42:08.513740+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15048","citing_title":"YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ","json":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ.json","graph_json":"https://pith.science/api/pith-number/TFWLEHT3I4RYWTXQMJONR74DEZ/graph.json","events_json":"https://pith.science/api/pith-number/TFWLEHT3I4RYWTXQMJONR74DEZ/events.json","paper":"https://pith.science/paper/TFWLEHT3"},"agent_actions":{"view_html":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ","download_json":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ.json","view_paper":"https://pith.science/paper/TFWLEHT3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.07227&json=true","fetch_graph":"https://pith.science/api/pith-number/TFWLEHT3I4RYWTXQMJONR74DEZ/graph.json","fetch_events":"https://pith.science/api/pith-number/TFWLEHT3I4RYWTXQMJONR74DEZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ/action/storage_attestation","attest_author":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ/action/author_attestation","sign_citation":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ/action/citation_signature","submit_replication":"https://pith.science/pith/TFWLEHT3I4RYWTXQMJONR74DEZ/action/replication_record"}},"created_at":"2026-07-05T08:42:08.513740+00:00","updated_at":"2026-07-05T08:42:08.513740+00:00"}