{"id":"https://openalex.org/W2251003032","doi":"https://doi.org/10.1145/2740908.2742476","title":"Locally Adaptive Density Ratio for Detecting Novelty in Twitter Streams","display_name":"Locally Adaptive Density Ratio for Detecting Novelty in Twitter Streams","publication_year":2015,"publication_date":"2015-05-18","ids":{"openalex":"https://openalex.org/W2251003032","doi":"https://doi.org/10.1145/2740908.2742476","mag":"2251003032"},"language":"en","primary_location":{"id":"doi:10.1145/2740908.2742476","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2740908.2742476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024379867","display_name":"Yun-Qian Miao","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yun-Qian Miao","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013166935","display_name":"Ahmed Farahat","orcid":"https://orcid.org/0000-0002-9828-8051"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ahmed K. Farahat","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039318050","display_name":"Mohamed S. Kamel","orcid":"https://orcid.org/0000-0001-6173-8082"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mohamed S. Kamel","raw_affiliation_strings":["University of Waterloo, Waterloo, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Waterloo, Waterloo, ON, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"799","last_page":"804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.995199978351593,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7683867812156677},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.7249470949172974},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6332399845123291},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6144970059394836},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.5480709671974182},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5373894572257996},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.444087952375412},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4381937086582184},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4254205822944641},{"id":"https://openalex.org/keywords/data-stream-mining","display_name":"Data stream mining","score":0.4211602807044983},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35815727710723877},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12286913394927979},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0944526195526123}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7683867812156677},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.7249470949172974},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6332399845123291},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6144970059394836},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.5480709671974182},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5373894572257996},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.444087952375412},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4381937086582184},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4254205822944641},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.4211602807044983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35815727710723877},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12286913394927979},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0944526195526123},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C27206212","wikidata":"https://www.wikidata.org/wiki/Q34178","display_name":"Theology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2740908.2742476","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2740908.2742476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 24th International Conference on World Wide Web","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W53209706","https://openalex.org/W197049292","https://openalex.org/W288980890","https://openalex.org/W1601068082","https://openalex.org/W1977005232","https://openalex.org/W1982029265","https://openalex.org/W1998927233","https://openalex.org/W2026037750","https://openalex.org/W2031434482","https://openalex.org/W2065255264","https://openalex.org/W2082250201","https://openalex.org/W2090708982","https://openalex.org/W2105497548","https://openalex.org/W2112483442","https://openalex.org/W2118013824","https://openalex.org/W2118882002","https://openalex.org/W2131584489","https://openalex.org/W2144182447","https://openalex.org/W2152322845","https://openalex.org/W2153635508","https://openalex.org/W2158698691","https://openalex.org/W2401963278","https://openalex.org/W2405765071","https://openalex.org/W2811380766","https://openalex.org/W2913243980","https://openalex.org/W2918757710","https://openalex.org/W2963800105"],"related_works":["https://openalex.org/W1998441453","https://openalex.org/W4297926828","https://openalex.org/W2113751036","https://openalex.org/W2034758081","https://openalex.org/W2106649047","https://openalex.org/W127176861","https://openalex.org/W2105725268","https://openalex.org/W2108349310","https://openalex.org/W3017097308","https://openalex.org/W3122040920"],"abstract_inverted_index":{"With":[0],"the":[1,14,21,29,58,74,84,94,102,118,128,131],"massive":[2],"growth":[3],"of":[4,16,31,130],"social":[5],"data,":[6],"a":[7,49,77],"huge":[8],"attention":[9],"has":[10],"been":[11],"given":[12],"to":[13,68,88,100,124],"task":[15],"detecting":[17],"key":[18],"topics":[19,41],"in":[20,42,56],"Twitter":[22],"stream.":[23],"In":[24,45,98],"this":[25],"paper,":[26],"we":[27,47,108],"propose":[28,48],"use":[30],"novelty":[32],"detection":[33],"techniques":[34],"for":[35,53,113],"identifying":[36],"both":[37],"emerging":[38,90],"and":[39,63,72],"evolving":[40,70],"new":[43,62],"tweets.":[44],"specific,":[46],"locally":[50,78],"adaptive":[51,79],"approach":[52,112],"density-ratio":[54,85,120],"estimation":[55],"which":[57],"density":[59],"ratio":[60],"between":[61],"reference":[64],"data":[65],"is":[66,81],"used":[67],"capture":[69,89],"novelties,":[71],"at":[73],"same":[75],"time":[76],"kernel":[80],"employed":[82],"into":[83],"objective":[86],"function":[87],"novelties":[91],"based":[92],"on":[93],"local":[95],"neighborhood":[96],"structure.":[97],"order":[99],"address":[101],"challenges":[103],"associated":[104],"with":[105,117],"short":[106],"text,":[107],"adopt":[109],"an":[110],"efficient":[111],"calculating":[114],"semantic":[115],"kernels":[116],"proposed":[119,132],"method.":[121],"A":[122],"comparison":[123],"different":[125],"methods":[126],"shows":[127],"superiority":[129],"algorithm.":[133]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
