{"id":"https://openalex.org/W2940969404","doi":"https://doi.org/10.3366/cor.2019.0162","title":"Incorporating text dispersion into keyword analyses","display_name":"Incorporating text dispersion into keyword analyses","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2940969404","doi":"https://doi.org/10.3366/cor.2019.0162","mag":"2940969404"},"language":"en","primary_location":{"id":"doi:10.3366/cor.2019.0162","doi":"https://doi.org/10.3366/cor.2019.0162","is_oa":false,"landing_page_url":"https://doi.org/10.3366/cor.2019.0162","pdf_url":null,"source":{"id":"https://openalex.org/S120559332","display_name":"Corpora","issn_l":"1749-5032","issn":["1749-5032","1755-1676"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","erih-plus","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320189","host_organization_name":"Edinburgh University Press","host_organization_lineage":["https://openalex.org/P4310320189","https://openalex.org/P4310321110"],"host_organization_lineage_names":["Edinburgh University Press","University of Edinburgh"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Corpora","raw_type":"journal-article"},"volume":null,"type":"article","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/A5033473979","display_name":"Jesse Egbert","orcid":"https://orcid.org/0000-0002-3751-2865","observed_orcids":["https://orcid.org/0000-0002-3751-2865"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jesse Egbert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042954371","display_name":"Doug Biber","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doug Biber","raw_affiliation_strings":["English"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"English","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":32.413,"has_fulltext":false,"cited_by_count":169,"citation_normalized_percentile":{"value":0.99482515,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"14","issue":"1","first_page":"77","last_page":"104"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.7476000189781189,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.7476000189781189,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10366","display_name":"Discourse Analysis in Language Studies","score":0.10890000313520432,"subfield":{"id":"https://openalex.org/subfields/1208","display_name":"Literature and Literary Theory"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.039799999445676804,"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/keyword-analysis","display_name":"keyword analysis","score":0.9229999780654907},{"id":"https://openalex.org/keywords/keyness","display_name":"keyness","score":0.6790000200271606},{"id":"https://openalex.org/keywords/corpus-frequency","display_name":"corpus frequency","score":0.652999997138977},{"id":"https://openalex.org/keywords/discourse-domains","display_name":"discourse domains","score":0.597000002861023},{"id":"https://openalex.org/keywords/keyword-lists","display_name":"keyword lists","score":0.5059999823570251}],"concepts":[{"id":"https://openalex.org/C47385372","wikidata":"https://www.wikidata.org/wiki/Q7098943","display_name":"Optimal distinctiveness theory","level":2,"score":0.8838459849357605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225208878517151},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.6023377776145935},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5688111782073975},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5538139939308167},{"id":"https://openalex.org/C177562468","wikidata":"https://www.wikidata.org/wiki/Q182893","display_name":"Dispersion (optics)","level":2,"score":0.4997379779815674},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.49530354142189026},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4892692267894745},{"id":"https://openalex.org/C66882249","wikidata":"https://www.wikidata.org/wiki/Q169336","display_name":"Homogeneous","level":2,"score":0.48062580823898315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4748670756816864},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4579535722732544},{"id":"https://openalex.org/C532629269","wikidata":"https://www.wikidata.org/wiki/Q865083","display_name":"Corpus linguistics","level":2,"score":0.42602425813674927},{"id":"https://openalex.org/C162446236","wikidata":"https://www.wikidata.org/wiki/Q653137","display_name":"Content analysis","level":2,"score":0.41455739736557007},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.41258007287979126},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.18412944674491882},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16692790389060974},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10735052824020386},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08109942078590393},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.07453018426895142},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3366/cor.2019.0162","doi":"https://doi.org/10.3366/cor.2019.0162","is_oa":false,"landing_page_url":"https://doi.org/10.3366/cor.2019.0162","pdf_url":null,"source":{"id":"https://openalex.org/S120559332","display_name":"Corpora","issn_l":"1749-5032","issn":["1749-5032","1755-1676"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","erih-plus","jufo-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310320189","host_organization_name":"Edinburgh University Press","host_organization_lineage":["https://openalex.org/P4310320189","https://openalex.org/P4310321110"],"host_organization_lineage_names":["Edinburgh University Press","University of Edinburgh"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Corpora","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.6000000238418579,"display_name":"Quality education","id":"https://metadata.un.org/sdg/4"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W33901952","https://openalex.org/W61988534","https://openalex.org/W122202277","https://openalex.org/W1514302479","https://openalex.org/W1532510989","https://openalex.org/W1575967963","https://openalex.org/W1969351512","https://openalex.org/W1999123300","https://openalex.org/W2005179178","https://openalex.org/W2006514889","https://openalex.org/W2042006448","https://openalex.org/W2062946872","https://openalex.org/W2093920276","https://openalex.org/W2116780029","https://openalex.org/W2133518763","https://openalex.org/W2134943861","https://openalex.org/W2138069620","https://openalex.org/W2140798747","https://openalex.org/W2326584371","https://openalex.org/W2889478606","https://openalex.org/W2917344761","https://openalex.org/W2969453936","https://openalex.org/W4205905939","https://openalex.org/W4242104588","https://openalex.org/W4290649644"],"related_works":["https://openalex.org/W3130309674","https://openalex.org/W4367042089","https://openalex.org/W1712611402","https://openalex.org/W4414842272","https://openalex.org/W7167667805","https://openalex.org/W7005368762","https://openalex.org/W7140712142","https://openalex.org/W2062946872","https://openalex.org/W4287447445","https://openalex.org/W4395455898"],"abstract_inverted_index":{"Keyword":[0],"analysis":[1,111],"has":[2,45],"become":[3],"an":[4],"indispensable":[5],"tool":[6],"for":[7,55,109,138,188],"discourse":[8,26,96],"analysts,":[9],"being":[10],"applied":[11],"to":[12,38,104,134,148,169],"identify":[13,149],"the":[14,21,30,56,63,72,82,94,129,150],"words":[15],"that":[16,85,117,152,180],"are":[17,68,76,88,153,166],"especially":[18],"characteristic":[19],"of":[20,33,58,84,93,100,131,145,155,161],"texts":[22,83],"in":[23,65,71],"a":[24,43,52,61,106,143,185],"target":[25,95],"domain.":[27,97],"But,":[28],"surprisingly,":[29],"statistical":[31],"computation":[32,57],"keyness":[34,115,183],"makes":[35],"no":[36],"reference":[37],"those":[39],"texts.":[40],"Rather,":[41],"once":[42],"corpus":[44,86,125],"been":[46],"constructed,":[47],"it":[48],"is":[49,103,118,184],"treated":[50],"as":[51],"homogeneous":[53],"whole":[54],"keyness.":[59],"As":[60],"result,":[62],"keywords":[64,151],"such":[66],"lists":[67],"relatively":[69],"frequent":[70],"corpus,":[73],"but":[74],"they":[75],"often":[77],"not":[78,90],"widely":[79],"dispersed":[80],"across":[81],"and":[87,163,177],"thus":[89],"truly":[91],"representative":[92],"The":[98],"purpose":[99],"this":[101,132],"study":[102],"propose":[105],"new":[107],"method":[108],"keyword":[110,190],"\u2013":[112,116],"text":[113,121,181],"dispersion":[114,182],"based":[119,173],"on":[120,174],"dispersion,":[122],"rather":[123],"than":[124],"frequency.":[126],"We":[127],"compare":[128,170],"effectiveness":[130],"measure":[133,187],"four":[135],"other":[136],"methods":[137,172],"computing":[139],"keyness,":[140],"carrying":[141],"out":[142,168],"series":[144],"case":[146],"studies":[147],"typical":[154],"online":[156],"travel":[157],"blogs.":[158],"A":[159],"variety":[160],"quantitative":[162],"qualitative":[164],"analyses":[165],"carried":[167],"these":[171],"their":[175],"content-generalisability":[176],"content-distinctiveness,":[178],"demonstrating":[179],"superior":[186],"generating":[189],"lists.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":26},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":14},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":11},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":15}],"updated_date":"2026-10-23T09:35:12.469056","created_date":"2019-05-03T00:00:00"}