Social media provide a potentially useful new data source to understand emerging public health behaviors. In this paper, we study messages about e-cigarettes posted to Twitter.com. We apply methods to classify messages by sentiment and to estimate the gender and age of users. We apply our approach to nearly one million messages about e-cigarettes posted from October 2012 to September 2013. We find that overall volume of e-cigarette tweets increased five-fold (from 30K per month to 150K per month); and that males and younger users were more likely to post positive messages about e-cigarettes. A qualitative analysis also reveals several trends, such as negative sentiment toward people who smoke in class; females giving e-cigarettes to relatives to help them quit smoking; and spikes in people using e-cigarettes to quit smoking in January.


  author =       {Elaine Cristina Resende and Aron Culotta},
  booktitle = {Workshop on Computational Health Science at the 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics},
  title = {A demographic and sentiment analysis of e-cigarette messages on Twitter},
  year = 2015