{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:EFO5JJBTXS26X5OUCKUEPVQZRE","short_pith_number":"pith:EFO5JJBT","schema_version":"1.0","canonical_sha256":"215dd4a433bcb5ebf5d412a847d6198939f46a71d30787bce837c98a850ec178","source":{"kind":"arxiv","id":"1411.2674","version":3},"attestation_state":"computed","paper":{"title":"The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"Charles Blundell, Fangjian Guo, Hanna Wallach, Katherine Heller","submitted_at":"2014-11-11T01:02:20Z","abstract_excerpt":"We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesi"},"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":"1411.2674","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2014-11-11T01:02:20Z","cross_cats_sorted":["cs.CL","cs.LG","cs.SI"],"title_canon_sha256":"b7ffae3ed38f8d121e9d11c88afa8f8e1027cdf6e4a1bc9d1c6784767971b75d","abstract_canon_sha256":"fd1c41e97ee92fbfa3a7d89494881b1c19c0807628d21eb2376364c677cd4b5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:28:40.163821Z","signature_b64":"h8U+OY081zDRK3eZfqqWUoUdIE6CaD0NJIKBFzGtOPQvPapUdhcztxFzK79pfijKoGo+E6JblLfsZHGADTjzCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"215dd4a433bcb5ebf5d412a847d6198939f46a71d30787bce837c98a850ec178","last_reissued_at":"2026-05-18T02:28:40.163284Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:28:40.163284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Bayesian Echo Chamber: Modeling Social Influence via Linguistic Accommodation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.SI"],"primary_cat":"stat.ML","authors_text":"Charles Blundell, Fangjian Guo, Hanna Wallach, Katherine Heller","submitted_at":"2014-11-11T01:02:20Z","abstract_excerpt":"We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.2674","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1411.2674","created_at":"2026-05-18T02:28:40.163374+00:00"},{"alias_kind":"arxiv_version","alias_value":"1411.2674v3","created_at":"2026-05-18T02:28:40.163374+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.2674","created_at":"2026-05-18T02:28:40.163374+00:00"},{"alias_kind":"pith_short_12","alias_value":"EFO5JJBTXS26","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_16","alias_value":"EFO5JJBTXS26X5OU","created_at":"2026-05-18T12:28:25.294606+00:00"},{"alias_kind":"pith_short_8","alias_value":"EFO5JJBT","created_at":"2026-05-18T12:28:25.294606+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/EFO5JJBTXS26X5OUCKUEPVQZRE","json":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE.json","graph_json":"https://pith.science/api/pith-number/EFO5JJBTXS26X5OUCKUEPVQZRE/graph.json","events_json":"https://pith.science/api/pith-number/EFO5JJBTXS26X5OUCKUEPVQZRE/events.json","paper":"https://pith.science/paper/EFO5JJBT"},"agent_actions":{"view_html":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE","download_json":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE.json","view_paper":"https://pith.science/paper/EFO5JJBT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1411.2674&json=true","fetch_graph":"https://pith.science/api/pith-number/EFO5JJBTXS26X5OUCKUEPVQZRE/graph.json","fetch_events":"https://pith.science/api/pith-number/EFO5JJBTXS26X5OUCKUEPVQZRE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE/action/storage_attestation","attest_author":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE/action/author_attestation","sign_citation":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE/action/citation_signature","submit_replication":"https://pith.science/pith/EFO5JJBTXS26X5OUCKUEPVQZRE/action/replication_record"}},"created_at":"2026-05-18T02:28:40.163374+00:00","updated_at":"2026-05-18T02:28:40.163374+00:00"}