{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:01:05Z","timestamp":1792785665007,"version":"4.5.0"},"reference-count":24,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Bundesministerium f\u00fcr Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz","award":["3621 EMF 106"],"award-info":[{"award-number":["3621 EMF 106"]}],"id":[{"id":"https:\/\/ror.org\/01yj5ad85","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Radiol. Prot."],"published-print":{"date-parts":[[2026,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Public perception of health risks from electromagnetic fields (EMF) and 5G mobile networks diverges persistently from the scientific evidence base, a gap increasingly shaped by social media. While existing research has examined the acute COVID-5G conspiracy episode on individual platforms, systematic multi-platform analyses of the broader EMF misinformation landscape remain scarce. This study presents a mixed-methods analysis of EMF and 5G-related misinformation across YouTube, Telegram, and Twitter\/X in German-language social media, combining automated classification, sentiment analysis, and topic modelling with structured qualitative content analysis. The three corpora comprise over 430 000 posts from more than 16 000 accounts, covering content published primarily between 2019 and 2024. The analysis reveals that EMF misinformation circulates in a predominantly pseudo-expert communicative register rather than overtly inflammatory language, is structurally embedded within broader conspiracy ecosystems rather than circulating as standalone content, and is sustained by commercial incentive structures alongside ideological motivations. The three platforms serve complementary functional roles within an integrated distribution network. While the pandemic-era peak in activity has subsided, the underlying network infrastructure remains intact. The findings establish an empirical baseline for the German-language EMF misinformation landscape against which future developments, including the impact of generative AI on misinformation production, can be measured.<\/jats:p>","DOI":"10.1088\/1361-6498\/aea624","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T22:48:02Z","timestamp":1789166882000},"page":"041502","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond the 5G-COVID moment: the scope of EMF mis- and disinformation on German-language social media"],"prefix":"10.1088","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3377-655X","authenticated-orcid":true,"given":"Tobias","family":"Eder","sequence":"first","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2931-456X","authenticated-orcid":true,"given":"Katja","family":"Aue","sequence":"additional","affiliation":[{"name":"C3 team"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-8407","authenticated-orcid":true,"given":"Larissa","family":"Drescher","sequence":"additional","affiliation":[{"name":"C3 team"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2885-0846","authenticated-orcid":false,"given":"Kerstin","family":"Dressel","sequence":"additional","affiliation":[{"name":"sine-Institut"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5942-2297","authenticated-orcid":true,"given":"Georg","family":"Groh","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3032-399X","authenticated-orcid":false,"given":"Jutta","family":"Roosen","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"jrpaea624bib1","doi-asserted-by":"publisher","DOI":"10.2196\/19458","type":"journal-article","article-title":"COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data","volume":"22","author":"Ahmed","year":"2020","journal-title":"J. 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