biostudies-literatureLee ERoyal SocietyHealth Research533-540https://www.ebi.ac.uk/biostudies/studies/S-EPMC9910155biostudies-literatureUnknown25(3)<h4>Introduction</h4>Advocates of electronic nicotine delivery systems (ENDS) increasingly use Twitter to promote liberal ENDS policies. "World Vape Day" (WVD) is an annual campaign organized by pro-ENDS advocacy groups, some of which have links to the nicotine industry (eg, via funding from the "Foundation for a Smoke-Free World"). In 2020, the campaign used dedicated social media accounts to disseminate WVD-branded images and campaign messages. We examined tweets posted as part of WVD 2020 to identify and analyze pro-ENDS policy arguments.<h4>Aims and methods</h4>We extracted tweets posted between 26 May and 3 June 2020 that included the hashtag #WorldVapeDay. We used qualitative thematic analysis to code a random sample (n = 2200) of approximately half the original English language tweeNicotine & tobacco research : official journal of the Society for Research on Nicotine and TobaccoAn Analysis of Arguments Advanced via Twitter in an Advocacy Campaign to Promote Electronic Nicotine Delivery Systems.PMC9910155UOO202819/641Evans-Reeves KFenton EJoshi ALee EHoek JRobertson LfalseAn Analysis of Arguments Advanced via Twitter in an Advocacy Campaign to Promote Electronic Nicotine Delivery Systems.<h4>Introduction</h4>Advocates of electronic nicotine delivery systems (ENDS) increasingly use Twitter to promote liberal ENDS policies. "World Vape Day" (WVD) is an annual campaign organized by pro-ENDS advocacy groups, some of which have links to the nicotine industry (eg, via funding from the "Foundation for a Smoke-Free World"). In 2020, the campaign used dedicated social media accounts to disseminate WVD-branded images and campaign messages. We examined tweets posted as part of WVD 2020 to identify and analyze pro-ENDS policy arguments.<h4>Aims and methods</h4>We extracted tweets posted between 26 May and 3 June 2020 that included the hashtag #WorldVapeDay. We used qualitative thematic analysis to code a random sample (n = 2200) of approximately half the original English language twee2023-01-01T00:00:00Z2023 Feb2026-06-03T13:24:39.329Z2024-12-04T03:35:40.836ZS-EPMC99101553626997810.1093/ntr/ntac237