**2. Methods**

We retrieved data from Twitter using NodeXL related to the time when the first study was published linking cancer and vaping in mice. NodeXL utilises the Search Application Programming Interface (AP)I. We retrieved data using the keywords "ecigarette" OR "e-cig" OR "ecig" OR "vaping", and we were able to retrieve a su fficient amount of tweets. The tweets in the network were tweeted over the 17 h, 37 min period from Monday, 07 October 2019, at 21:20 Coordinated Universal Time (UTC), to Tuesday, 08 October 2019, at 14:58 UTC. The graph represents a network of 14,912 Twitter users.

We utilised social network analysis, which is an established method of studying social media content, and a complete overview of network shapes and structures can be found elsewhere [17]. Within our network graph, there was an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that was not a

"replies-to" or "mentions". The graph was directed, and vertices were grouped by cluster using the Clauset–Newman–Moore cluster algorithm. The graph was laid out using the Harel–Koren fast multiscale layout algorithm.
