title: Mining social media data for opinion polarities about electronic cigarettes.
abstract: There is an ongoing debate about harm and benefit of e-cigarettes, usage of which has rapidly increased in recent years. By separating non-commercial (organic) tweets from commercial tweets, we seek to evaluate the general public's attitudes towards e-cigarettes.
  
  
  
  We collected tweets containing the words 'e-cig', 'e-cigarette', 'e-liquid', 'vape', 'vaping', 'vapor' and 'vaporizer' from 23 July to 14 October 2015 (n=757 167). A multilabel Naïve Bayes model was constructed to classify tweets into 5 polarities (against, support, neutral, commercial, irrelevant). We further analysed the prevalence of e-cigarette tweets, geographic variations in these tweets and the impact of socioeconomic factors on the public attitudes towards e-cigarettes.
  
  
  
  Opinions from organic tweets about e-cigarettes were mixed (against 17.7%, support 10.8% and neutral 19.4%). The organic-against tweets delivered strong educational information about the risks of e-cigarette use and advocated for the general public, especially youth, to stop vaping. However, the organic-against tweets were outnumbered by commercial tweets and organic-support tweets by a ratio of over 1 to 3. Higher prevalence of organic tweets was associated with states with higher education rates (r=0.60, p<0.0001), higher percentage of black and African-American population (r=0.34, p=0.01), and higher median household income (r=0.33, p=0.02). The support rates for e-cigarettes were associated with states with fewer persons under 18 years old (r=-0.33, p=0.02) and a higher percentage of female population (r=0.3, p=0.02).
  
  
  
  The organic-against tweets raised public awareness of potential health risks and could aid in preventing non-smokers, adolescents and young adults from using e-cigarettes. Opinion polarities about e-cigarettes from social networks could be highly influential to the general public, especially youth. Further educational campaigns should include measuring their effectiveness.
authors.affiliation: Health Services & Outcomes Research, Children's Mercy Hospital, Kansas City, Missouri, USA.|Department of Biomedical & Health Informatics, University of Missouri-Kansas City, Kansas City, Missouri, USA.|Department of Pediatrics, University of Missouri-Kansas City, Kansas City, Missouri, USA.
authors.forename: Hongying
authors.initials: H
authors.lastname: Dai
authors.affiliation: Bellevue University, Omaha, Nebraska, USA.
authors.forename: Jianqiang
authors.initials: J
authors.lastname: Hao
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D057225:Data Mining; D066300:Electronic Nicotine Delivery Systems / Q000191:economics / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D008297:Male; D011639:Public Opinion; D000074606:Smoking Prevention; D061108:Social Media; D012959:Socioeconomic Factors; D000072137:Vaping / Q000009:adverse effects / Q000706:statistics & numerical data*; D055815:Young Adult
keywords: Media; Social marketing; Socioeconomic status