{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2F3JFZD4DHSPP33LG22ME5GU3D","short_pith_number":"pith:2F3JFZD4","schema_version":"1.0","canonical_sha256":"d17692e47c19e4f7ef6b36b4c274d4d8e417d52b73389625f70ddfcd00845b61","source":{"kind":"arxiv","id":"2209.10464","version":2},"attestation_state":"computed","paper":{"title":"Quantifying attention via dwell time and engagement in a social media browsing environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.HC","authors_text":"David Rand, Gordon Pennycook, Hause Lin, Ziv Epstein","submitted_at":"2022-09-21T16:06:44Z","abstract_excerpt":"Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in t"},"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":"2209.10464","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2022-09-21T16:06:44Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"173d46b0103746ef410b1521985d027bdf63772da534aafc441c496211ea89e8","abstract_canon_sha256":"5429b53b2d5955ca757b863227b453360cd69a47040f5013d479217e16b79f3c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:57.276926Z","signature_b64":"Gs9pbAV6m13CERRnHBMI4EVydlcu/yx2ikQvSY0VRjmSuLIWBDvqskzR3nUUYJaeXduxRaN1Wv173gQVHJkxBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d17692e47c19e4f7ef6b36b4c274d4d8e417d52b73389625f70ddfcd00845b61","last_reissued_at":"2026-07-05T05:13:57.276419Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:57.276419Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantifying attention via dwell time and engagement in a social media browsing environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.HC","authors_text":"David Rand, Gordon Pennycook, Hause Lin, Ziv Epstein","submitted_at":"2022-09-21T16:06:44Z","abstract_excerpt":"Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.10464","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.10464/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":"2209.10464","created_at":"2026-07-05T05:13:57.276483+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.10464v2","created_at":"2026-07-05T05:13:57.276483+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.10464","created_at":"2026-07-05T05:13:57.276483+00:00"},{"alias_kind":"pith_short_12","alias_value":"2F3JFZD4DHSP","created_at":"2026-07-05T05:13:57.276483+00:00"},{"alias_kind":"pith_short_16","alias_value":"2F3JFZD4DHSPP33L","created_at":"2026-07-05T05:13:57.276483+00:00"},{"alias_kind":"pith_short_8","alias_value":"2F3JFZD4","created_at":"2026-07-05T05:13:57.276483+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.19995","citing_title":"A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D","json":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D.json","graph_json":"https://pith.science/api/pith-number/2F3JFZD4DHSPP33LG22ME5GU3D/graph.json","events_json":"https://pith.science/api/pith-number/2F3JFZD4DHSPP33LG22ME5GU3D/events.json","paper":"https://pith.science/paper/2F3JFZD4"},"agent_actions":{"view_html":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D","download_json":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D.json","view_paper":"https://pith.science/paper/2F3JFZD4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.10464&json=true","fetch_graph":"https://pith.science/api/pith-number/2F3JFZD4DHSPP33LG22ME5GU3D/graph.json","fetch_events":"https://pith.science/api/pith-number/2F3JFZD4DHSPP33LG22ME5GU3D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D/action/storage_attestation","attest_author":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D/action/author_attestation","sign_citation":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D/action/citation_signature","submit_replication":"https://pith.science/pith/2F3JFZD4DHSPP33LG22ME5GU3D/action/replication_record"}},"created_at":"2026-07-05T05:13:57.276483+00:00","updated_at":"2026-07-05T05:13:57.276483+00:00"}