{"id":"https://openalex.org/W4284960486","doi":"https://doi.org/10.1007/s00180-022-01246-z","title":"Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data","display_name":"Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data","publication_year":2022,"publication_date":"2022-07-09","ids":{"openalex":"https://openalex.org/W4284960486","doi":"https://doi.org/10.1007/s00180-022-01246-z","pmid":"https://pubmed.ncbi.nlm.nih.gov/37223721"},"language":"en","primary_location":{"id":"doi:10.1007/s00180-022-01246-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-022-01246-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-022-01246-z.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00180-022-01246-z.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007902286","display_name":"Christoph Weisser","orcid":"https://orcid.org/0000-0003-0616-1027"},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Weisser","raw_affiliation_strings":["Campus-Institut Data Science (CIDAS), G\u00f6ttingen, Germany","Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Campus-Institut Data Science (CIDAS), G\u00f6ttingen, Germany","institution_ids":[]},{"raw_affiliation_string":"Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030190460","display_name":"Christoph Gerloff","orcid":null},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Gerloff","raw_affiliation_strings":["Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063790800","display_name":"Anton Thielmann","orcid":"https://orcid.org/0000-0002-6768-8992"},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Anton Thielmann","raw_affiliation_strings":["Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"],"raw_orcid":"https://orcid.org/0000-0002-6768-8992","affiliations":[{"raw_affiliation_string":"Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045331325","display_name":"Andr\u00e9 Python","orcid":"https://orcid.org/0000-0001-8094-7226"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Andre Python","raw_affiliation_strings":["Zhejiang University, Hangzhou, People's Republic of China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University, Hangzhou, People's Republic of China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067506028","display_name":"Arik Reuter","orcid":"https://orcid.org/0009-0002-7837-7011"},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Arik Reuter","raw_affiliation_strings":["Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009871877","display_name":"Thomas Kneib","orcid":"https://orcid.org/0000-0003-3390-0972"},"institutions":[{"id":"https://openalex.org/I74656192","display_name":"University of G\u00f6ttingen","ror":"https://ror.org/01y9bpm73","country_code":"DE","type":"education","lineage":["https://openalex.org/I74656192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Thomas Kneib","raw_affiliation_strings":["Campus-Institut Data Science (CIDAS), G\u00f6ttingen, Germany","Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Campus-Institut Data Science (CIDAS), G\u00f6ttingen, Germany","institution_ids":[]},{"raw_affiliation_string":"Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, Germany","institution_ids":["https://openalex.org/I74656192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016630281","display_name":"Benjamin S\u00e4fken","orcid":"https://orcid.org/0000-0003-4702-3333"},"institutions":[{"id":"https://openalex.org/I43980791","display_name":"Clausthal University of Technology","ror":"https://ror.org/04qb8nc58","country_code":"DE","type":"education","lineage":["https://openalex.org/I43980791"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Benjamin S\u00e4fken","raw_affiliation_strings":["Clausthal University of Technology, Clausthal-Zellerfeld, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Clausthal University of Technology, Clausthal-Zellerfeld, Germany","institution_ids":["https://openalex.org/I43980791"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5063790800"],"corresponding_institution_ids":["https://openalex.org/I74656192"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":4.0676,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.94533467,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"38","issue":"2","first_page":"647","last_page":"674"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.9855999946594238,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9854999780654907,"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/latent-dirichlet-allocation","display_name":"Latent Dirichlet allocation","score":0.9319237470626831},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.8452309370040894},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.6657758355140686},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6598563194274902},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5263643860816956},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.5142064094543457},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.502676248550415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41757088899612427},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.37621864676475525},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3596166968345642},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.27559101581573486},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2016730010509491},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.08016452193260193}],"concepts":[{"id":"https://openalex.org/C500882744","wikidata":"https://www.wikidata.org/wiki/Q269236","display_name":"Latent Dirichlet allocation","level":3,"score":0.9319237470626831},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.8452309370040894},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6657758355140686},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6598563194274902},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5263643860816956},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.5142064094543457},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.502676248550415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41757088899612427},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.37621864676475525},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3596166968345642},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27559101581573486},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2016730010509491},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.08016452193260193},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1007/s00180-022-01246-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-022-01246-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-022-01246-z.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},{"id":"pmid:37223721","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37223721","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computational statistics","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10060035","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10060035","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10060035/pdf/180_2022_Article_1246.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Comput Stat","raw_type":"Text"},{"id":"pmh:oai:RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01246-z","is_oa":false,"landing_page_url":"http://link.springer.com/10.1007/s00180-022-01246-z","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},{"id":"pmh:oai:publications.goettingen-research-online.de:2/113964","is_oa":true,"landing_page_url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/113964","pdf_url":null,"source":{"id":"https://openalex.org/S4306401634","display_name":"GoeScholar  The Publication Server of the Georg-August-Universit\u00e4t G\u00f6ttingen (Georg-August-Universit\u00e4t G\u00f6ttingen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210122495","host_organization_name":"Asklepios Klinik St. Georg","host_organization_lineage":["https://openalex.org/I4210122495"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1007/s00180-022-01246-z","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00180-022-01246-z","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00180-022-01246-z.pdf","source":{"id":"https://openalex.org/S8500805","display_name":"Computational Statistics","issn_l":"0943-4062","issn":["0943-4062","1613-9658"],"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":"Computational Statistics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7599999904632568,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321870","display_name":"Georg-August-Universit\u00e4t G\u00f6ttingen","ror":"https://ror.org/01y9bpm73"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4284960486.pdf","grobid_xml":"https://content.openalex.org/works/W4284960486.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W1981276685","https://openalex.org/W1987680958","https://openalex.org/W2004192095","https://openalex.org/W2027875450","https://openalex.org/W2033765726","https://openalex.org/W2038043464","https://openalex.org/W2061922307","https://openalex.org/W2097089247","https://openalex.org/W2114508388","https://openalex.org/W2225156818","https://openalex.org/W2250533720","https://openalex.org/W2560103719","https://openalex.org/W2572380545","https://openalex.org/W3021299154","https://openalex.org/W3096236010","https://openalex.org/W3199414990","https://openalex.org/W4229083187","https://openalex.org/W6813523509"],"related_works":["https://openalex.org/W2769501189","https://openalex.org/W4315588616","https://openalex.org/W4312773271","https://openalex.org/W2761847515","https://openalex.org/W130869231","https://openalex.org/W2888805565","https://openalex.org/W4293734197","https://openalex.org/W4206967254","https://openalex.org/W2131689821","https://openalex.org/W2352674739"],"abstract_inverted_index":{"Topic":[0],"models":[1,101,129,149],"are":[2,27,66,102,121],"a":[3,87,92],"useful":[4],"and":[5,17,58,97,146],"popular":[6],"method":[7],"to":[8,110],"find":[9,115],"latent":[10],"topics":[11,153],"of":[12,44,76,84,127,138],"documents.":[13],"However,":[14],"the":[15,30,42,45,51,59,74,77,82,100,111,125,144,155],"short":[16,96],"sparse":[18,70,98],"texts":[19],"in":[20],"social":[21],"media":[22],"micro-blogs":[23],"such":[24],"as":[25,86,132],"Twitter":[26],"challenging":[28],"for":[29,69,124],"most":[31],"commonly":[32],"used":[33,123],"Latent":[34],"Dirichlet":[35,54],"Allocation":[36],"(LDA)":[37],"topic":[38,48,128,148],"model.":[39,158],"We":[40,114],"compare":[41,73],"performance":[43,75],"standard":[46,117,156],"LDA":[47,157],"model":[49],"with":[50,95],"Gibbs":[52],"Sampler":[53],"Multinomial":[55],"Model":[56,63],"(GSDMM)":[57],"Gamma":[60],"Poisson":[61],"Mixture":[62],"(GPM),":[64],"which":[65],"specifically":[67],"designed":[68],"data.":[71],"To":[72],"three":[78],"models,":[79],"we":[80],"propose":[81],"simulation":[83],"pseudo-documents":[85],"novel":[88],"evaluation":[89,126,134],"method.":[90],"In":[91],"case":[93],"study":[94],"text,":[99],"evaluated":[103],"on":[104],"tweets":[105],"filtered":[106],"by":[107],"keywords":[108],"relating":[109],"Covid-19":[112],"pandemic.":[113],"that":[116,120,143],"coherence":[118],"scores":[119],"often":[122],"perform":[130],"poorly":[131],"an":[133],"metric.":[135],"The":[136],"results":[137],"our":[139],"simulation-based":[140],"approach":[141],"suggest":[142],"GSDMM":[145],"GPM":[147],"may":[150],"generate":[151],"better":[152],"than":[154]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}