Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter.

•Supervised models are evaluated for identifying e-cigarette proponents on Twitter.•Both profile bio and recent tweets play a role in improving model performance.•Proponents can be identified with an F-score of 91% and accuracy of 96%.•Proponents’ tweet at least ten times more frequently on four favorable e-cig themes.