Whose Post Is It? Predicting E-cigarette Brand from Social Media Posts.

Clicks: 339
ID: 89577
2018
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
E-cigarette advertisers know that 76% of youth use social media, yet little is known about the nature of e-cigarette advertising on social media most favored by youth. We utilized text-mining to characterize e-cigarette advertising and marketing messages from image-focused social media brand sites, and to construct and test an algorithm for predicting brand from brand-generated social media posts.Data comprised 5022 unique posts accompanied by an image from Facebook, Instagram or Pinterest e-cigarette brand pages for Blu, Logic, Metro, and NJOY from February 2012 to April 2015. Text-tokenization was used to quantify text for use as predictors in analyses.Blu had the largest social media presence (65%), followed by Logic (16%), NJOY (12%) and Metro (7%). Blu's average post length was significantly shorter than all other brands. Words most commonly used in posts differed by brand. Regression analyses successfully differentiated Blu and NJOY brands from other brands.Analyses revealed e-cigarette brands used different types of messages to appeal to social media users. Whereas words used by Blu and NJOY sold a "lifestyle," words used by Logic and Metro relied on device and product identification.
Reference Key
vandewater2018whosetobacco Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Vandewater, Elizabeth A;Clendennen, Stephanie L;Hébert, Emily T;Bigman, Galya;Jackson, Christian D;Wilkinson, Anna V;Perry, Cheryl L;
Journal tobacco regulatory science
Year 2018
DOI
10.18001/TRS.4.2.3
URL
Keywords
social media youth marketing text analytics text-mining e-cigarette brands text-tokenization

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.