{"id":"https://openalex.org/W7204811216","doi":"https://doi.org/10.18653/2026.emnlp-main.1725","title":"AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not","display_name":"AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204811216","doi":"https://doi.org/10.18653/2026.emnlp-main.1725"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.1725","doi":"https://doi.org/10.18653/2026.emnlp-main.1725","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1725","pdf_url":"https://aclanthology.org/2026.emnlp-main.1725.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.1725.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040865271","display_name":"Zhengyang Shan","orcid":"https://orcid.org/0009-0001-9291-6416","observed_orcids":["https://orcid.org/0009-0001-9291-6416","https://orcid.org/0009-0008-5777-3289"]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengyang Shan","raw_affiliation_strings":["Boston University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001230649","display_name":"Yukyung Lee","orcid":"https://orcid.org/0000-0002-7835-6336","observed_orcids":["https://orcid.org/0000-0002-7835-6336"]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yukyung Lee","raw_affiliation_strings":["Boston University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":["https://openalex.org/I111088046"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113067502","display_name":"Sophie Hao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I111088046","display_name":"Boston University","ror":"https://ror.org/05qwgg493","country_code":"US","type":"education","lineage":["https://openalex.org/I111088046"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sophie Hao","raw_affiliation_strings":["Boston University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Boston University","institution_ids":["https://openalex.org/I111088046"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I111088046"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6930777,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"39352","last_page":"39376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12380","display_name":"Authorship Attribution and Profiling","score":0.9976000189781189,"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/T12380","display_name":"Authorship Attribution and Profiling","score":0.9976000189781189,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.0006000000284984708,"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/T13629","display_name":"Text Readability and Simplification","score":9.999999747378752e-05,"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/stylometry","display_name":"stylometry","score":0.8040000200271606},{"id":"https://openalex.org/keywords/ai-generated-text","display_name":"AI-generated text","score":0.8270000219345093},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.9160000085830688},{"id":"https://openalex.org/keywords/lexical-diversity","display_name":"lexical diversity","score":0.843999981880188},{"id":"https://openalex.org/keywords/entropy-information-theory","display_name":"entropy (information theory)","score":0.7129999995231628},{"id":"https://openalex.org/keywords/lexical-density","display_name":"lexical density","score":0.9959999918937683},{"id":"https://openalex.org/keywords/human-written-text","display_name":"human-written text","score":0.7229999899864197},{"id":"https://openalex.org/keywords/text-classification","display_name":"text classification","score":0.3160000145435333}],"concepts":[{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.625},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5928000211715698},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5776000022888184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5730000138282776},{"id":"https://openalex.org/C2987487971","wikidata":"https://www.wikidata.org/wiki/Q8096","display_name":"Lexical access","level":3,"score":0.42660000920295715},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.42480000853538513},{"id":"https://openalex.org/C2781202465","wikidata":"https://www.wikidata.org/wiki/Q18346297","display_name":"Lexical diversity","level":3,"score":0.4113999903202057},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C126706616","wikidata":"https://www.wikidata.org/wiki/Q2944660","display_name":"Lexical item","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C2474386","wikidata":"https://www.wikidata.org/wiki/Q461183","display_name":"Text corpus","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C2993724205","wikidata":"https://www.wikidata.org/wiki/Q315","display_name":"Human language","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C11192451","wikidata":"https://www.wikidata.org/wiki/Q2032038","display_name":"Stylometry","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C3018949938","wikidata":"https://www.wikidata.org/wiki/Q17166101","display_name":"Text messaging","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.2590000033378601}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-main.1725","doi":"https://doi.org/10.18653/2026.emnlp-main.1725","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1725","pdf_url":"https://aclanthology.org/2026.emnlp-main.1725.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.27855","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.27855","pdf_url":"https://arxiv.org/pdf/2608.27855","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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.27855","doi":"https://doi.org/10.48550/arxiv.2608.27855","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.27855","pdf_url":null,"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,"listed_in":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.1725","doi":"https://doi.org/10.18653/2026.emnlp-main.1725","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.1725","pdf_url":"https://aclanthology.org/2026.emnlp-main.1725.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204811216.pdf","grobid_xml":"https://content.openalex.org/works/W7204811216.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4416371309","https://openalex.org/W4409746924","https://openalex.org/W7154567091","https://openalex.org/W7214798026","https://openalex.org/W7215507565","https://openalex.org/W4400207838","https://openalex.org/W4412430923","https://openalex.org/W7158423939","https://openalex.org/W4414742150","https://openalex.org/W4416131863"],"abstract_inverted_index":{"Text":[0],"generated":[1],"by":[2],"large":[3],"language":[4],"models":[5],"(LLMs)":[6],"has":[7],"been":[8],"shown":[9],"to":[10,40,45,111,143],"be":[11,190],"stylometrically":[12],"distinct":[13],"from":[14,83,156,167],"human-written":[15,168],"text":[16,42,82,155,158],"(Andr\u00e9":[17],"et":[18,21,25,31],"al.,":[19,22,26,32],"2023;Shah":[20],"2023;Opara,":[23],"2024;Soto":[24],"2024;Li":[27],"and":[28,49,76,89,101,125,181,188],"Zhang,":[29],"2025;Selvioglu":[30],"2025).But":[33],"LLMs":[34,88],"are":[35,160],"increasingly":[36],"used":[37],"not":[38,106,176],"only":[39,118],"generate":[41],"but":[43,159],"also":[44],"edit":[46],"human":[47,84],"writing,":[48],"it":[50,166],"is":[51,175],"unclear":[52],"whether":[53],"the":[54,57,93,99,108,132,147],"two":[55],"leave":[56,183],"same":[58,109],"trace.We":[59],"show":[60,117],"that":[61,135,172],"AI":[62,137],"generation":[63,180],"leaves":[64],"a":[65,69,119,126,177],"consistent":[66],"\"stylometric":[67],"footprint\":":[68],"small":[70,120],"subset":[71],"of":[72],"features,":[73],"primarily":[74],"entropy":[75],"lexical":[77,123],"diversity,":[78],"consistently":[79],"separates":[80],"AIgenerated":[81],"writing":[85],"across":[86],"8":[87],"5":[90],"domains,":[91],"while":[92],"remaining":[94],"features":[95,151],"depend":[96],"heavily":[97],"on":[98],"domain":[100],"generator.AI":[102],"editing,":[103],"however,":[104],"does":[105],"reproduce":[107],"footprint.Relative":[110],"their":[112],"humanwritten":[113],"sources,":[114],"AI-edited":[115,154],"texts":[116],"increase":[121,134],"in":[122,128],"diversity":[124],"decrease":[127],"entropy,":[129],"rather":[130],"than":[131],"joint":[133],"characterizes":[136],"generation.Lexical":[138],"density,":[139],"which":[140],"contributes":[141],"little":[142],"generation,":[144],"instead":[145],"becomes":[146],"dominant":[148],"editing-associated":[149],"signal.Stylometric":[150],"therefore":[152],"separate":[153],"AI-generated":[157],"substantially":[161],"less":[162],"effective":[163],"at":[164],"separating":[165],"text.Our":[169],"results":[170],"suggest":[171],"\"AI":[173],"text\"":[174],"single":[178],"phenomenon:":[179],"editing":[182],"qualitatively":[184],"different":[185],"stylometric":[186],"traces":[187],"should":[189],"studied":[191],"separately.":[192]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2026-09-01T00:00:00"}