March 18, 2014

Using twitter to examine smoking behavior and perceptions of emerging tobacco products.

OBJECTIVE: To develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes.

METHODS: We collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012.

RESULTS: Sentiment toward tobacco was overall more positive (1939/4215, 46% of tweets) than negative (1349/4215, 32%) or neutral among tweets mentioning it, even excluding the 9% of tweets categorized as marketing.

CONCLUSIONS: Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment.

J Med Internet Res. 2013;15(8):e174