{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:AHR6QHJJ65VPNXITGFAQSTL5JS","short_pith_number":"pith:AHR6QHJJ","schema_version":"1.0","canonical_sha256":"01e3e81d29f76af6dd133141094d7d4caf73e79a0aa6866df2000cbc9634b380","source":{"kind":"arxiv","id":"1811.05063","version":1},"attestation_state":"computed","paper":{"title":"SMERC: Social media event response clustering using textual and temporal information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","physics.data-an","stat.AP"],"primary_cat":"cs.SI","authors_text":"Caitlin Gray, Giang T. Nguyen, Lewis Mitchell, Nigel G.Bean, Peter Mathews","submitted_at":"2018-11-13T01:58:36Z","abstract_excerpt":"Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a probabilistic approach to incorporate temporal information. By analysing the distribution of time gaps between tweets we show that the gaps between pairs of related tweets exhibit exponential decay, whereas the gaps between unrelated tweets are approximately uniform. Guided by this insight, we use probabilistic arguments to estimate the likelihood that a pair of tweets are related, and build an improved clustering metho"},"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":"1811.05063","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-11-13T01:58:36Z","cross_cats_sorted":["cs.IR","physics.data-an","stat.AP"],"title_canon_sha256":"28ba21cb1278c424a98ab4cf4d21eed40aba9f348825f0e22f64e1fe74b91ad2","abstract_canon_sha256":"01ae20001339bf3b0d86132966222ddc8191a967b460fd038fefff9a4d9562ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:01.306749Z","signature_b64":"76S6YFYKoOhn8/brj7+tlj8MKx4ZtRC4izFEymyEqByVyyyaYS5Wn3he91m4+yjYNDePZ0Q5A/0F0J6m/+ymCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01e3e81d29f76af6dd133141094d7d4caf73e79a0aa6866df2000cbc9634b380","last_reissued_at":"2026-05-18T00:01:01.306181Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:01.306181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SMERC: Social media event response clustering using textual and temporal information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","physics.data-an","stat.AP"],"primary_cat":"cs.SI","authors_text":"Caitlin Gray, Giang T. Nguyen, Lewis Mitchell, Nigel G.Bean, Peter Mathews","submitted_at":"2018-11-13T01:58:36Z","abstract_excerpt":"Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a probabilistic approach to incorporate temporal information. By analysing the distribution of time gaps between tweets we show that the gaps between pairs of related tweets exhibit exponential decay, whereas the gaps between unrelated tweets are approximately uniform. Guided by this insight, we use probabilistic arguments to estimate the likelihood that a pair of tweets are related, and build an improved clustering metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05063","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":""},"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":"1811.05063","created_at":"2026-05-18T00:01:01.306275+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05063v1","created_at":"2026-05-18T00:01:01.306275+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05063","created_at":"2026-05-18T00:01:01.306275+00:00"},{"alias_kind":"pith_short_12","alias_value":"AHR6QHJJ65VP","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_16","alias_value":"AHR6QHJJ65VPNXIT","created_at":"2026-05-18T12:32:13.499390+00:00"},{"alias_kind":"pith_short_8","alias_value":"AHR6QHJJ","created_at":"2026-05-18T12:32:13.499390+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/AHR6QHJJ65VPNXITGFAQSTL5JS","json":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS.json","graph_json":"https://pith.science/api/pith-number/AHR6QHJJ65VPNXITGFAQSTL5JS/graph.json","events_json":"https://pith.science/api/pith-number/AHR6QHJJ65VPNXITGFAQSTL5JS/events.json","paper":"https://pith.science/paper/AHR6QHJJ"},"agent_actions":{"view_html":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS","download_json":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS.json","view_paper":"https://pith.science/paper/AHR6QHJJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05063&json=true","fetch_graph":"https://pith.science/api/pith-number/AHR6QHJJ65VPNXITGFAQSTL5JS/graph.json","fetch_events":"https://pith.science/api/pith-number/AHR6QHJJ65VPNXITGFAQSTL5JS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS/action/storage_attestation","attest_author":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS/action/author_attestation","sign_citation":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS/action/citation_signature","submit_replication":"https://pith.science/pith/AHR6QHJJ65VPNXITGFAQSTL5JS/action/replication_record"}},"created_at":"2026-05-18T00:01:01.306275+00:00","updated_at":"2026-05-18T00:01:01.306275+00:00"}