{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T04:24:48Z","timestamp":1754108688439,"version":"3.32.0"},"reference-count":32,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"unspecified","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ASHE"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:title>Abstract<\/jats:title>\n\t  <jats:sec id=\"S2732494X24004716_as1\">\n\t    <jats:title>Objective:<\/jats:title>\n\t    <jats:p>Social media has become an important tool in monitoring infectious disease outbreaks such as coronavirus disease 2019 and highly pathogenic avian influenza (HPAI). Influenced by the recent announcement of a possible human death from H5N2 avian influenza, we analyzed tweets collected from X (formerly Twitter) to describe the messaging regarding the HPAI outbreak, including mis- and dis-information, concerns, and health education.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2732494X24004716_as2\">\n\t    <jats:title>Methods:<\/jats:title>\n\t    <jats:p>We collected tweets involving keywords relating to HPAI for 5 days (June 04 to June 08, 2024). Using topic modeling, emotion, sentiment, and user demographic analyses, we were able to describe the population and the HPAI-related topics that users discussed.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2732494X24004716_as3\">\n\t    <jats:title>Results:<\/jats:title>\n\t    <jats:p>With an original pool of 14,796 tweets, we analyzed a final data set of 13,319 tweets from 10,421 unique X users, with 50.4% of the tweets exhibiting negative sentiments (&lt; 0 on a scale of \u22124 to +4). Predominant emotions were anger and fear shown in 36.4% and 29.5% of tweets, respectively. We identified 5 distinct, descriptive topics within the tweets. The use of emotionally charged language and spread of misinformation were substantial.<\/jats:p>\n\t  <\/jats:sec>\n\t  <jats:sec id=\"S2732494X24004716_as4\">\n\t    <jats:title>Conclusions:<\/jats:title>\n\t    <jats:p>Mis- and dis-information about the causes of and ways to prevent HPAI infections were common. A large portion of the tweets contained references to a planned epidemic or \u201cplandemic\u201d to influence the upcoming 2024 US presidential election. These tweets were countered by a limited number of tweets discussing infection locations, case reports, and preventive measures. Our study can be used by public health officials and clinicians to influence the discourse on current and future outbreaks.<\/jats:p>\n\t  <\/jats:sec>","DOI":"10.1017\/ash.2024.471","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T09:37:18Z","timestamp":1736415438000},"source":"Crossref","is-referenced-by-count":2,"title":["Birds of a feather? Mis- and dis-information on the social media platform X related to avian influenza"],"prefix":"10.1017","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4000-9192","authenticated-orcid":false,"given":"Lauren N.","family":"Cooper","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marlon I.","family":"Diaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0909-9396","authenticated-orcid":false,"given":"John J.","family":"Hanna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zachary M.","family":"Most","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9559-4646","authenticated-orcid":false,"given":"Christoph U.","family":"Lehmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard J.","family":"Medford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"S2732494X24004716_ref26","unstructured":"26. Liedke, J , Wang, L. Social Media and News Fact Sheet. 2023. https:\/\/www.pewresearch.org\/journalism\/fact-sheet\/social-media-and-news-fact-sheet\/#who-consumes-news-on-each-social-media-site. Accessed 07\/10\/2024, 2024."},{"key":"S2732494X24004716_ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313684"},{"key":"S2732494X24004716_ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.vaccine.2023.06.058"},{"key":"S2732494X24004716_ref3","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2020.406"},{"key":"S2732494X24004716_ref21","unstructured":"21. Hung, YZ. GitHub - zhunhung\/Python-SentiStrength. https:\/\/github.com\/zhunhung\/Python-SentiStrength. Accessed 07\/15\/2024, 2024."},{"key":"S2732494X24004716_ref19","unstructured":"19. ChatGPT. ChatGPT. 2024. Large Language Model. https:\/\/chatgpt.com\/. Accessed 07\/02\/2024, 2024."},{"key":"S2732494X24004716_ref22","unstructured":"22. Aman Gupta, AB , Sharma, Shivam , Bilakhiya, Karan . 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Accessed 07\/15\/2024, 2024."}],"container-title":["Antimicrobial Stewardship &amp; Healthcare Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S2732494X24004716","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T09:37:25Z","timestamp":1736415445000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S2732494X24004716\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]}},"alternative-id":["S2732494X24004716"],"URL":"https:\/\/doi.org\/10.1017\/ash.2024.471","relation":{},"ISSN":["2732-494X"],"issn-type":[{"value":"2732-494X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"article-number":"e4"}}