Introduction:
A growing body of literature has examined the content of vaping-related tweets, although less is known about the people who generate and disseminate these messages, and the role of e-cigarette advocates.

Objectives:
To conduct a content analysis of tweets to identify key conversation trends and patterns over time; and discern the core voices, message frames and sentiment surrounding e-cigarette discussions by Australian Twitter users.

Methods:
4,432 tweets from 2012, 2014, 2016 and 2018 were analysed using a tri-axial classification system (user category, sentiment, tweet content). Data were collected through TrISMA (Tracking Infrastructure for Social Media Analysis) via a list of 15 popular e-cigarette related terms.

Results:
Vape retailers and manufactures and vape proponents contributed to 49% of tweets analysed. These groups’ tweets were overwhelmingly positive (>97%). Forty-six percent of tweets were classified as advertising or promotion, with vape retailers and manufacturers (49%) and vape proponents (20%) contributing the largest proportion. A number of tweets (3%) asserted a conspiracy against vapers and vaping, believing the Government is not legalising vaping to protect the tobacco industry and the income the Government receives through tobacco excise; and that those working within public health are spreading lies and misinformation. Vape proponents (58%) were also found to challenge anyone who expressed critical-vaping views (2.0%).

Conclusion(s):
Content on Twitter presents a tilted conversation encouraging e-cigarette use, promoting vaping as a socially acceptable practice, discredits scientific evidence for health risks, and rallies around the idea that e-cigarettes should largely be outside the bounds of policy. The accumulation of individual beliefs in these unfounded stories, conspiracy theories, and pseudoscience can give rise to social movements with profound consequences for global public health. Deployment of innovative methods on a broader scale is needed, such as natural language processing and assisted data mining to track and counter the spread of misinformation.

Funding Acknowledgments:
This work was supported by a Healthway Exploratory Research Grant (grant number 32803) and an Australian Government Research Training Program Scholarship. The Scholarship is provided by the Commonwealth of Australia to support general living costs for students (KM) undertaking Research Doctorate studies. All funders had no role in the design of the study; in the collection, analyses, or interpretation of data; and in the decision to present the results. This research was also supported by infrastructure provided through the Australian Research Council-funded project TrISMA: Tracking Infrastructure for Social Media Analysis (LIEF grant LE140100148).