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Surv."],"published-print":{"date-parts":[[2026,9,30]]},"abstract":"<jats:p>As chatbots have become more commonplace writing tools, a need exists to understand the breadth of research about the humanness of machine-generated text via techniques that extend beyond the traditional Turing Test, in both dialogue (e.g., conversing with a chatbot) and non-dialogue (e.g., reading a news article) scenarios. To fill this gap and support future work, we survey current literature that examines and identifies humanness features of written communication generated with the state-of-the-art generative pre-trained transformer language models, provide a working definition of humanness, propose a text-based humanness taxonomy based on linguistic properties, and identify current research gaps.<\/jats:p>","DOI":"10.1145\/3806206","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T11:21:09Z","timestamp":1774956069000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparing the Humanness of Machine-Generated and Human-Authored Text"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3247-1248","authenticated-orcid":false,"given":"Autumn","family":"Toney","sequence":"first","affiliation":[{"name":"Computer Science, Georgetown University","place":["Washington, United States"]},{"name":"Maastricht University","place":["Washington, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9859-2918","authenticated-orcid":false,"given":"Leticia","family":"Bode","sequence":"additional","affiliation":[{"name":"Communication, Culture & Technology, Georgetown University","place":["Washington, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2162-814X","authenticated-orcid":false,"given":"Tiago","family":"Ventura","sequence":"additional","affiliation":[{"name":"Computational Social Science, Georgetown University","place":["Washington, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5128-9890","authenticated-orcid":false,"given":"Ethan","family":"Wilcox","sequence":"additional","affiliation":[{"name":"Computational Linguistics, Georgetown University","place":["Washington, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8300-2970","authenticated-orcid":false,"given":"Lisa","family":"Singh","sequence":"additional","affiliation":[{"name":"Computer Science, Georgetown University","place":["Washington, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"issue":"8","key":"e_1_3_3_2_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3548772","article-title":"Deep learning in sentiment analysis: Recent architectures","volume":"55","author":"Abdullah Tariq","year":"2022","unstructured":"Tariq Abdullah and Ahmed Ahmet. 2022. 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