Literature DB >> 26980151

Mining social media data for opinion polarities about electronic cigarettes.

Hongying Dai1,2,3, Jianqiang Hao4.   

Abstract

BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Entities:  

Keywords:  Media; Social marketing; Socioeconomic status

Mesh:

Year:  2016        PMID: 26980151     DOI: 10.1136/tobaccocontrol-2015-052818

Source DB:  PubMed          Journal:  Tob Control        ISSN: 0964-4563            Impact factor:   7.552


  15 in total

1.  Geographic variations in electronic cigarette advertisements on Twitter in the United States.

Authors:  Hongying Dai; Michael J Deem; Jianqiang Hao
Journal:  Int J Public Health       Date:  2016-10-14       Impact factor: 3.380

2.  Social media e-cigarette exposure and e-cigarette expectancies and use among young adults.

Authors:  Pallav Pokhrel; Pebbles Fagan; Thaddeus A Herzog; Linnea Laestadius; Wayne Buente; Crissy T Kawamoto; Hye-Ryeon Lee; Jennifer B Unger
Journal:  Addict Behav       Date:  2017-11-08       Impact factor: 3.913

Review 3.  Overview of Electronic Nicotine Delivery Systems: A Systematic Review.

Authors:  Allison M Glasser; Lauren Collins; Jennifer L Pearson; Haneen Abudayyeh; Raymond S Niaura; David B Abrams; Andrea C Villanti
Journal:  Am J Prev Med       Date:  2016-11-30       Impact factor: 5.043

4.  Talking about tobacco on Twitter is associated with tobacco product use.

Authors:  Jennifer B Unger; Robert Urman; Tess Boley Cruz; Anuja Majmundar; Jessica Barrington-Trimis; Mary Ann Pentz; Rob McConnell
Journal:  Prev Med       Date:  2018-06-10       Impact factor: 4.018

5.  A longitudinal analysis of electronic cigarette forum participation.

Authors:  Sarah F Maloney; Eric K Soule; Sherilyn Palafox; Keaton McFadden; Mignonne C Guy; Thomas Eissenberg; Pebbles Fagan
Journal:  Addict Behav       Date:  2018-08-07       Impact factor: 4.591

Review 6.  Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.

Authors:  Su Golder; Robin Stevens; Karen O'Connor; Richard James; Graciela Gonzalez-Hernandez
Journal:  J Med Internet Res       Date:  2022-04-29       Impact factor: 7.076

7.  Mining online e-liquid reviews for opinion polarities about e-liquid features.

Authors:  Zhipeng Chen; Daniel D Zeng
Journal:  BMC Public Health       Date:  2017-07-07       Impact factor: 3.295

8.  E-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends.

Authors:  Jon-Patrick Allem; Emilio Ferrara; Sree Priyanka Uppu; Tess Boley Cruz; Jennifer B Unger
Journal:  JMIR Public Health Surveill       Date:  2017-12-20

9.  E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

Authors:  Allison J Lazard; Adam J Saffer; Gary B Wilcox; Arnold DongWoo Chung; Michael S Mackert; Jay M Bernhardt
Journal:  JMIR Public Health Surveill       Date:  2016-12-12

10.  A qualitative exploration of information-seeking by electronic nicotine delivery systems (ENDS) users in New Zealand.

Authors:  Lindsay Robertson; Janet Hoek; Mei-Ling Blank; Rosalina Richards; Pamela Ling; Lucy Popova; Lydia McMillan
Journal:  BMJ Open       Date:  2018-10-25       Impact factor: 2.692

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