OBJECTIVES: Twitter, a popular social media outlet, has become a useful tool for the study of social behavior through user interactions called tweets. The location time , and message content of tweets provide invaluable social and demographic information for an applied comparison of social behaviors across the world. Our goal is to determine the density and sentiment surrounding tobacco and e-cigarette tweets and link prevalence of word choices to tobacco and e-cigarette use at various localities. METHODS:  All tweets with geo-spatial coordinates are salvaged from the twitter-feed, representing approximately 1% of the entire twitter-sphere. Pattern matching by tobacco and e-cigarette related keywords yield approximately 20,000 affiliated tweets per month from North America.  The emotionally charged words that contribute to the positivity of various subsets of regional tweets are quantitatively measured using hedonometrics.  We examined the density of these behavioral tweet indicators by region and tested the relationship between tweeted smoking sentiments and time-space-type coordinates over a 4-month span.  RESULTS:  For states with ≥600 tobacco related tweets (N=30), we find a strong positive correlation (Pearson’s r=0.54,  p<0.01) between the relative tweet density per state and the average positivity of tobacco related tweets.  However, state-to-state sentiment comparisons suggest the attitude toward tobacco use can vary. We also explore the relationship between the ratio of tobacco tweets per state-to-state smoking rate estimates.  Our results illustrate significant variation in smoking sentiments by state and at varying regional scopes. CONCLUSIONS: It is anticipated that real-time analysis of nicotine and tobacco products using tweets will allow for more targeted forms of health policy planning and intervention.   Regional density of nicotine and tobacco use related tweets yield insight to the prevalence of tobacco usage per capita. Sentiment analysis across the twitter-sphere can help illuminate hazardous health behavioral trends, which may lead to better targeting of health behavior interventions.