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