{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5THKORFHHWFHTSWEEHJDG6F3MG","short_pith_number":"pith:5THKORFH","schema_version":"1.0","canonical_sha256":"eccea744a73d8a79cac421d23378bb618f3a37c3c9e881ce7d19ce72babe2287","source":{"kind":"arxiv","id":"2404.16066","version":2},"attestation_state":"computed","paper":{"title":"Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.HC","authors_text":"Gabriella M. Harari, Heinrich Peters, Joseph B. Bayer, Sandra C. Matz, Sumer S. Vaid, Yikun Chi","submitted_at":"2024-04-20T16:36:28Z","abstract_excerpt":"The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii)"},"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":"2404.16066","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-04-20T16:36:28Z","cross_cats_sorted":["cs.LG","cs.SI"],"title_canon_sha256":"a62466bc1b85a68bb2370b956abbfb118fb27853058376d8762c173733973d55","abstract_canon_sha256":"83e1352d7f601c8e3bfe29f4cec490eebbc6c88bf4e7373904a77ab08e2db269"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:35:45.711592Z","signature_b64":"1Pr+aoBHG5u2tfp4gNEFyrFWtR/hfPH0utepdfT2C7jVHXefCikXSO7xC4RqWX3CYYWRcOgtVq1rA+om0gJJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eccea744a73d8a79cac421d23378bb618f3a37c3c9e881ce7d19ce72babe2287","last_reissued_at":"2026-07-05T08:35:45.711119Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:35:45.711119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SI"],"primary_cat":"cs.HC","authors_text":"Gabriella M. Harari, Heinrich Peters, Joseph B. Bayer, Sandra C. Matz, Sumer S. Vaid, Yikun Chi","submitted_at":"2024-04-20T16:36:28Z","abstract_excerpt":"The present paper introduces a novel approach to studying social media habits through predictive modeling of sequential smartphone user behaviors. While much of the literature on media and technology habits has relied on self-report questionnaires and simple behavioral frequency measures, we examine an important yet understudied aspect of media and technology habits: their embeddedness in repetitive behavioral sequences. Leveraging Long Short-Term Memory (LSTM) and transformer neural networks, we show that (i) social media use is predictable at the within and between-person level and that (ii)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16066","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/2404.16066/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":"2404.16066","created_at":"2026-07-05T08:35:45.711182+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16066v2","created_at":"2026-07-05T08:35:45.711182+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16066","created_at":"2026-07-05T08:35:45.711182+00:00"},{"alias_kind":"pith_short_12","alias_value":"5THKORFHHWFH","created_at":"2026-07-05T08:35:45.711182+00:00"},{"alias_kind":"pith_short_16","alias_value":"5THKORFHHWFHTSWE","created_at":"2026-07-05T08:35:45.711182+00:00"},{"alias_kind":"pith_short_8","alias_value":"5THKORFH","created_at":"2026-07-05T08:35:45.711182+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG","json":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG.json","graph_json":"https://pith.science/api/pith-number/5THKORFHHWFHTSWEEHJDG6F3MG/graph.json","events_json":"https://pith.science/api/pith-number/5THKORFHHWFHTSWEEHJDG6F3MG/events.json","paper":"https://pith.science/paper/5THKORFH"},"agent_actions":{"view_html":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG","download_json":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG.json","view_paper":"https://pith.science/paper/5THKORFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16066&json=true","fetch_graph":"https://pith.science/api/pith-number/5THKORFHHWFHTSWEEHJDG6F3MG/graph.json","fetch_events":"https://pith.science/api/pith-number/5THKORFHHWFHTSWEEHJDG6F3MG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG/action/storage_attestation","attest_author":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG/action/author_attestation","sign_citation":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG/action/citation_signature","submit_replication":"https://pith.science/pith/5THKORFHHWFHTSWEEHJDG6F3MG/action/replication_record"}},"created_at":"2026-07-05T08:35:45.711182+00:00","updated_at":"2026-07-05T08:35:45.711182+00:00"}