{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:4LZLJCF666K6ZCNYQHJOZJ5Z3G","short_pith_number":"pith:4LZLJCF6","schema_version":"1.0","canonical_sha256":"e2f2b488bef795ec89b881d2eca7b9d98993e98973fea13631100992008433be","source":{"kind":"arxiv","id":"1505.05657","version":1},"attestation_state":"computed","paper":{"title":"Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Adrien Guille, Cecile Favre","submitted_at":"2015-05-21T09:43:09Z","abstract_excerpt":"The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to de"},"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":"1505.05657","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2015-05-21T09:43:09Z","cross_cats_sorted":[],"title_canon_sha256":"26a6598461de39a183bb38250a439a6ecf10ac0d08c14d9433ec7d8ea531534b","abstract_canon_sha256":"602b255f0d4d8e3c918ac4efb46c059f91f49ec96ccd129e553de6190624a173"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:03:53.845294Z","signature_b64":"/0dM+1TOL1a1y//nydGR3RfCslT6EfAy3V2098P5ygylZrufh2vXc29nTM9se6gK6AruyuMmQ5KjaVJ/JByKAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2f2b488bef795ec89b881d2eca7b9d98993e98973fea13631100992008433be","last_reissued_at":"2026-05-18T02:03:53.844556Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:03:53.844556Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SI","authors_text":"Adrien Guille, Cecile Favre","submitted_at":"2015-05-21T09:43:09Z","abstract_excerpt":"The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.05657","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":"1505.05657","created_at":"2026-05-18T02:03:53.844693+00:00"},{"alias_kind":"arxiv_version","alias_value":"1505.05657v1","created_at":"2026-05-18T02:03:53.844693+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.05657","created_at":"2026-05-18T02:03:53.844693+00:00"},{"alias_kind":"pith_short_12","alias_value":"4LZLJCF666K6","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_16","alias_value":"4LZLJCF666K6ZCNY","created_at":"2026-05-18T12:29:05.191682+00:00"},{"alias_kind":"pith_short_8","alias_value":"4LZLJCF6","created_at":"2026-05-18T12:29:05.191682+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/4LZLJCF666K6ZCNYQHJOZJ5Z3G","json":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G.json","graph_json":"https://pith.science/api/pith-number/4LZLJCF666K6ZCNYQHJOZJ5Z3G/graph.json","events_json":"https://pith.science/api/pith-number/4LZLJCF666K6ZCNYQHJOZJ5Z3G/events.json","paper":"https://pith.science/paper/4LZLJCF6"},"agent_actions":{"view_html":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G","download_json":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G.json","view_paper":"https://pith.science/paper/4LZLJCF6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1505.05657&json=true","fetch_graph":"https://pith.science/api/pith-number/4LZLJCF666K6ZCNYQHJOZJ5Z3G/graph.json","fetch_events":"https://pith.science/api/pith-number/4LZLJCF666K6ZCNYQHJOZJ5Z3G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G/action/storage_attestation","attest_author":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G/action/author_attestation","sign_citation":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G/action/citation_signature","submit_replication":"https://pith.science/pith/4LZLJCF666K6ZCNYQHJOZJ5Z3G/action/replication_record"}},"created_at":"2026-05-18T02:03:53.844693+00:00","updated_at":"2026-05-18T02:03:53.844693+00:00"}