{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T16:21:47Z","timestamp":1777998107434,"version":"3.51.4"},"reference-count":39,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Science Foundation","award":["IIS-1851591"],"award-info":[{"award-number":["IIS-1851591"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Digital Threats"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>Automated journalism technology is transforming news production and changing how audiences perceive the news. As automated text-generation models advance, it is important to understand how readers perceive human-written and machine-generated content. This study used OpenAI\u2019s GPT-2 text-generation model (May 2019 release) and articles from news organizations across the political spectrum to study participants\u2019 reactions to human- and machine-generated articles. As participants read the articles, we collected their facial expression and galvanic skin response (GSR) data together with self-reported perceptions of article source and content credibility. We also asked participants to identify their political affinity and assess the articles\u2019 political tone to gain insight into the relationship between political leaning and article perception. Our results indicate that the May 2019 release of OpenAI\u2019s GPT-2 model generated articles that were misidentified as written by a human close to half the time, while human-written articles were identified correctly as written by a human about 70 percent of the time.<\/jats:p>","DOI":"10.1145\/3428158","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T10:07:19Z","timestamp":1618913239000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Perceptions of Human and Machine-Generated Articles"],"prefix":"10.1145","volume":"2","author":[{"given":"Shubhra","family":"Tewari","sequence":"first","affiliation":[{"name":"Whitman College, Walla Walla, WA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renos","family":"Zabounidis","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4901-6121","authenticated-orcid":false,"given":"Ammina","family":"Kothari","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reynold","family":"Bailey","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecilia Ovesdotter","family":"Alm","sequence":"additional","affiliation":[{"name":"Rochester Institute of Technology, Rochester, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"19","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Baly Ramy","year":"2019","unstructured":"Ramy Baly , Georgi Karadzhov , Abdelrhman Saleh , James Glass , and Preslav Nakov . 2019 . Multi-task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 2109--2116. DOI:https:\/\/doi.org\/10. 18653\/v1\/N 19 - 1216 Ramy Baly, Georgi Karadzhov, Abdelrhman Saleh, James Glass, and Preslav Nakov. 2019. Multi-task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 2109--2116. 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