{"id":"https://openalex.org/W3034618340","doi":"https://doi.org/10.1007/s10994-021-05988-7","title":"Embed2Detect: temporally clustered embedded words for event detection in social media","display_name":"Embed2Detect: temporally clustered embedded words for event detection in social media","publication_year":2021,"publication_date":"2021-05-24","ids":{"openalex":"https://openalex.org/W3034618340","doi":"https://doi.org/10.1007/s10994-021-05988-7","mag":"3034618340"},"language":"en","primary_location":{"id":"doi:10.1007/s10994-021-05988-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-05988-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-05988-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-05988-7.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hansi Hettiarachchi","orcid":"https://orcid.org/0000-0003-4609-5001"},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Hansi Hettiarachchi","raw_affiliation_strings":["School of Computing and Digital Technology, Birmingham City University, Birmingham, UK"],"raw_orcid":"https://orcid.org/0000-0003-4609-5001","affiliations":[{"raw_affiliation_string":"School of Computing and Digital Technology, Birmingham City University, Birmingham, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Mariam Adedoyin-Olowe","orcid":null},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mariam Adedoyin-Olowe","raw_affiliation_strings":["School of Computing and Digital Technology, Birmingham City University, Birmingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Digital Technology, Birmingham City University, Birmingham, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jagdev Bhogal","orcid":null},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jagdev Bhogal","raw_affiliation_strings":["School of Computing and Digital Technology, Birmingham City University, Birmingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Digital Technology, Birmingham City University, Birmingham, UK","institution_ids":["https://openalex.org/I12870472"]}]},{"author_position":"last","author":{"id":null,"display_name":"Mohamed Medhat Gaber","orcid":null},"institutions":[{"id":"https://openalex.org/I12870472","display_name":"Birmingham City University","ror":"https://ror.org/00t67pt25","country_code":"GB","type":"education","lineage":["https://openalex.org/I12870472"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mohamed Medhat Gaber","raw_affiliation_strings":["School of Computing and Digital Technology, Birmingham City University, Birmingham, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing and Digital Technology, Birmingham City University, Birmingham, UK","institution_ids":["https://openalex.org/I12870472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I12870472"],"apc_list":{"value":2780,"currency":"USD","value_usd":2780},"apc_paid":{"value":2780,"currency":"USD","value_usd":2780},"fwci":2.9485,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.91691461,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"111","issue":"1","first_page":"49","last_page":"87"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.6017000079154968,"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":0.6017000079154968,"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/T14347","display_name":"Big Data and Digital Economy","score":0.03240000084042549,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.0272000003606081,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/social-media","display_name":"Social media","score":0.7857000231742859},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.7799000144004822},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5195000171661377},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4869000017642975},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.46779999136924744},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4487999975681305},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.3400999903678894}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8043000102043152},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.7857000231742859},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.7799000144004822},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5195000171661377},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49320000410079956},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4869000017642975},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4487999975681305},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3628999888896942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3531000018119812},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C123606473","wikidata":"https://www.wikidata.org/wiki/Q907918","display_name":"Complex event processing","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.32409998774528503},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3174999952316284},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C143275388","wikidata":"https://www.wikidata.org/wiki/Q92438","display_name":"Microblogging","level":3,"score":0.31470000743865967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2872999906539917},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1007/s10994-021-05988-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-05988-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-05988-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2006.05908","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2006.05908","pdf_url":"https://arxiv.org/pdf/2006.05908","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:www.open-access.bcu.ac.uk:11982","is_oa":false,"landing_page_url":"http://www.open-access.bcu.ac.uk/11982/1/Hettiarachchi2021_Article_Embed2DetectTemporallyClustere.pdf","pdf_url":"http://www.open-access.bcu.ac.uk/11982/1/Hettiarachchi2021_Article_Embed2DetectTemporallyClustere.pdf","source":{"id":"https://openalex.org/S4306402654","display_name":"BCU Open Access Repository (Birmingham City University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I12870472","host_organization_name":"Birmingham City University","host_organization_lineage":["https://openalex.org/I12870472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Article"}],"best_oa_location":{"id":"doi:10.1007/s10994-021-05988-7","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10994-021-05988-7","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10994-021-05988-7.pdf","source":{"id":"https://openalex.org/S62148650","display_name":"Machine Learning","issn_l":"0885-6125","issn":["0885-6125","1573-0565"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034618340.pdf","grobid_xml":"https://content.openalex.org/works/W3034618340.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W153361609","https://openalex.org/W153416840","https://openalex.org/W165525898","https://openalex.org/W179875071","https://openalex.org/W1842080445","https://openalex.org/W1860364451","https://openalex.org/W1981974552","https://openalex.org/W1982029265","https://openalex.org/W1983498087","https://openalex.org/W1994817155","https://openalex.org/W2000311527","https://openalex.org/W2062088920","https://openalex.org/W2084591134","https://openalex.org/W2101196063","https://openalex.org/W2125099364","https://openalex.org/W2134008243","https://openalex.org/W2143745167","https://openalex.org/W2153579005","https://openalex.org/W2160654919","https://openalex.org/W2168400688","https://openalex.org/W2250539671","https://openalex.org/W2252278997","https://openalex.org/W2273718352","https://openalex.org/W2337150356","https://openalex.org/W2493916176","https://openalex.org/W2557700421","https://openalex.org/W2584497185","https://openalex.org/W2592662402","https://openalex.org/W2595177560","https://openalex.org/W2614548997","https://openalex.org/W2734575238","https://openalex.org/W2737086878","https://openalex.org/W2745188823","https://openalex.org/W2789202651","https://openalex.org/W2789429023","https://openalex.org/W2883724305","https://openalex.org/W2887568436","https://openalex.org/W2911870147","https://openalex.org/W2912817604","https://openalex.org/W2945076599","https://openalex.org/W2963341956","https://openalex.org/W2963418282","https://openalex.org/W2966515073","https://openalex.org/W2966969968","https://openalex.org/W2979826702","https://openalex.org/W2981204173","https://openalex.org/W3002312850","https://openalex.org/W3038098779","https://openalex.org/W4213009331","https://openalex.org/W6680532216"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Social":[1],"media":[2,22,58,142,187],"is":[3,11,39,62],"becoming":[4],"a":[5,82,134,171],"primary":[6],"medium":[7],"to":[8,64,77,161,202],"discuss":[9],"what":[10],"happening":[12],"around":[13],"the":[14,17,29,33,47,66,78,105,122,145,192,200,221,227,237,242],"world.":[15],"Therefore,":[16],"data":[18,38,54,59,102,188,229,244],"generated":[19],"by":[20,143],"social":[21,57,141,186],"platforms":[23],"contain":[24],"rich":[25],"information":[26,115],"which":[27,110,190],"describes":[28],"ongoing":[30],"events.":[31],"Further,":[32],"timeliness":[34],"associated":[35],"with":[36],"these":[37],"capable":[40,211],"of":[41,53,107,156,212,250],"facilitating":[42],"immediate":[43],"insights.":[44],"However,":[45],"considering":[46],"dynamic":[48],"nature":[49],"and":[50,69,98,103,126,150,169,194,197,214,218,240],"high":[51],"volume":[52],"production":[55],"in":[56,101,140,147,175],"streams,":[60],"it":[61,219,246],"impractical":[63],"filter":[65],"events":[67],"manually":[68],"therefore,":[70],"automated":[71,90],"event":[72,91,138,167,216,223],"detection":[73,92,139,168,217,224],"mechanisms":[74],"are":[75,111],"invaluable":[76],"community.":[79],"Apart":[80],"from":[81,117],"few":[83],"notable":[84],"exceptions,":[85],"most":[86],"previous":[87,176],"research":[88],"on":[89,96,182],"have":[93],"focused":[94],"only":[95],"statistical":[97],"syntactical":[99],"features":[100,165],"lacked":[104],"involvement":[106],"underlying":[108],"semantics":[109],"important":[112],"for":[113,241],"effective":[114,213],"retrieval":[116],"text":[118],"since":[119],"they":[120],"represent":[121,191],"connections":[123],"between":[124],"words":[125],"their":[127],"meanings.":[128],"In":[129],"this":[130],"paper,":[131],"we":[132],"propose":[133],"novel":[135],"method":[136,181],"termedEmbed2Detectfor":[137],"combining":[144],"characteristics":[146],"word":[148,157],"embeddings":[149,158],"hierarchical":[151],"agglomerative":[152],"clustering.":[153],"The":[154,206],"adoption":[155],"givesEmbed2Detectthe":[159],"capability":[160],"incorporate":[162],"powerful":[163],"semantical":[164],"into":[166],"overcome":[170],"major":[172],"limitation":[173],"inherent":[174],"approaches.":[177],"We":[178],"experimented":[179],"our":[180],"two":[183],"recent":[184,222],"real":[185],"sets":[189],"sports":[193,228],"political":[195,243],"domain":[196],"also":[198],"compared":[199],"results":[201,208],"several":[203],"state-of-the-art":[204],"methods.":[205,225],"obtained":[207],"show":[209],"thatEmbed2Detectis":[210],"efficient":[215],"outperforms":[220],"For":[226],"set,":[230,245],"Embed2Detect":[231],"achieved":[232],"27%":[233],"higher":[234],"F-measure":[235],"than":[236],"best-performed":[238],"baseline":[239],"was":[247],"an":[248],"increase":[249],"29%.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":6}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2020-06-19T00:00:00"}
