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Accepted for/Published in: JMIR Formative Research

Date Submitted: Oct 14, 2023
Date Accepted: Mar 6, 2024

The final, peer-reviewed published version of this preprint can be found here:

Social Network Analysis of e-Cigarette–Related Social Media Influencers on Twitter/X: Observational Study

Zhou R, Xie Z, Tang Q, Li D

Social Network Analysis of e-Cigarette–Related Social Media Influencers on Twitter/X: Observational Study

JMIR Form Res 2024;8:e53666

DOI: 10.2196/53666

PMID: 38557555

PMCID: 11019427

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Social Network Analysis of E-cigarette-related Social Media Influencers on Twitter: Observational Study

  • Runtao Zhou; 
  • Zidian Xie; 
  • Qihang Tang; 
  • Dongmei Li

ABSTRACT

Background:

An electronic cigarette (e-cigarette) uses a battery to heat e-liquid to generate an aerosol for consumers to inhale. E-cigarette use (vaping) has been associated with respiratory disease, cardiovascular disease, and cognitive functions. Recently, vaping has been popular, especially among youth and young adults.

Objective:

This study aims to understand social networks of social media (Twitter) influencers related to e-cigarettes through social network analysis.

Methods:

Through the Twitter streaming application programming interface (API), we identified 3,617,766 unique Twitter accounts posting e-cigarette-related tweets from May 3rd, 2021, to June 10th, 2022. From them, we identified 33 e-cigarette influencers. Furthermore, we identified and grouped the followers of these influencers into e-cigarette followers and non-e-cigarette followers. We randomly sampled 100 user accounts from e-cigarette followers and non-e-cigarette followers, respectively, and created corresponding social networks for each e-cigarette influencer. We compared different network measures (such as the clustering coefficient) between e-cigarette follower networks and non-e-cigarette follower networks.

Results:

For the identified 33 influencers, their e-cigarette follower networks have significantly higher clustering coefficients than their non-e-cigarette follower networks, 0.398 vs 0.098 (P<.001). The number of followers of e-cigarette followers is significantly higher than that of non-e-cigarette followers (322 vs. 201), with a P<.001. The number of tweets posted by e-cigarette follower networks is significantly higher (93 vs. 43, P<.001) than that by non-e-cigarette follower networks. Two major topics discussed in e-cigarette follower networks include promoting e-cigarette products or vaping activity and vaping helping with smoking cessation and harm reduction.

Conclusions:

Our study showed that e-cigarette followers of e-cigarette influencers are more closely connected than non-e-cigarette followers, providing a potentially practical intervention approach for future anti-vaping campaigns.


 Citation

Please cite as:

Zhou R, Xie Z, Tang Q, Li D

Social Network Analysis of e-Cigarette–Related Social Media Influencers on Twitter/X: Observational Study

JMIR Form Res 2024;8:e53666

DOI: 10.2196/53666

PMID: 38557555

PMCID: 11019427

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