{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T15:56:04Z","timestamp":1790870164344,"version":"4.1.0"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":22,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100020891","name":"PSC-CUNY","doi-asserted-by":"publisher","award":["TRADB-55-132"],"award-info":[{"award-number":["TRADB-55-132"]}],"id":[{"id":"10.13039\/100020891","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020891","name":"PSC-CUNY","doi-asserted-by":"publisher","award":["#67432-00 55"],"award-info":[{"award-number":["#67432-00 55"]}],"id":[{"id":"10.13039\/100020891","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) can be used to measure sentiment and reading level, facilitating its use to improve nutrition communication, though AI has not yet been tested for such analysis of nutrition articles. The present study investigated this by looking at reading level and sentiment in magazines aimed at various demographics as determined with ChatGPT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Sentiment analysis was conducted on a sample of articles about nutrition from legacy media collected over 1 year. Reading level of the articles was determined using the Simple\u00a0Measure of Gobbledygook (SMOG) formula. Nutrition content sub-themes were examined for reading level and sentiment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Average sentiment ratings remained close to neutral (mean = 0.26\u2009\u00b1\u20092.05), with no consistent changes over time. Articles written at higher reading levels showed more positive sentiment values than those at lower reading levels. Of the twelve nutrition sub-themes were identified, the most prevalent were food\/diet recommendations for health (23.8%), articles about celebrity diets (15.6%), cooking and culinary articles (14.1%), and food insecurity (10.7%). Comparison of sub-samples of sentiment ratings or Simple Measure of Gobbledygook (SMOG) scores done by a human coder versus ChatGPT were not different (P = NS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Reading levels are relatively high overall. These varied by source periodical and theme as would be expected with editors targeting varied readership groups. Analysis by nutrition themes showed differences in sentiment that were concurrent with the type of content analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrated that AI such as ChatGPT can be utilized as a for assessment of sentiment and reading levels in nutrition articles.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooag188","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:34:30Z","timestamp":1788867270000},"source":"Crossref","is-referenced-by-count":0,"title":["Use of chat GPT for sentiment and readability analysis of nutrition articles in legacy media"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-4439","authenticated-orcid":false,"given":"Ann","family":"Gaba","sequence":"first","affiliation":[{"name":"Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York , New York, NY 10027,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2970-1959","authenticated-orcid":false,"given":"Safa","family":"Amir","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York , New York, NY 10027,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1004-470X","authenticated-orcid":false,"given":"Haley","family":"Liebman","sequence":"additional","affiliation":[{"name":"Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York , New York, NY 10027,","place":["United 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