Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Apr 13, 2018
Date Accepted: Jul 20, 2018
(closed for review but you can still tweet)

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

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

El Tantawi M, Al-Ansari A, AlSubaie A, Fathy A, Aly NM, Mohamed AS

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

J Med Internet Res 2018;20(9):e10781

DOI: 10.2196/10781

PMID: 30213781

PMCID: 6231799

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

  • Maha El Tantawi; 
  • Asim Al-Ansari; 
  • Abdulelah AlSubaie; 
  • Amr Fathy; 
  • Nourhan M Aly; 
  • Amira S. Mohamed

ABSTRACT

Background:

Increasing the reach of messages disseminated through Twitter promotes the success of Twitter-based health education campaigns.

Objective:

This study aimed to identify factors associated with reach in a dental Twitter network (1) initially and (2) sustainably at individual and network levels.

Methods:

We used instructors’ and students’ Twitter usernames from a Saudi dental school in 2016-2017 and applied Gephi (a social network analysis tool) and social media analytics to calculate user and network metrics. Content analysis was performed to identify users disseminating oral health information. The study outcomes were reach at baseline and sustainably over 1.5 years. The explanatory variables were indicators of popularity (number of followers, likes, tweets retweeted by others), communication pattern (number of tweets, retweets, replies, tweeting/ retweeting oral health information or not). Multiple logistic regression models were used to investigate associations.

Results:

Among dental users, 31.8% had reach at baseline and 62.9% at the end of the study, reaching a total of 749,923 and dropping to 37,169 users at the end. At an individual level, reach was associated with the number of followers (baseline: odds ratio, OR=1.003, 95% CI=1.001-1.005 and sustainability: OR=1.002, 95% CI=1.0001-1.003), likes (baseline: OR=1.001, 95% CI=1.0001-1.002 and sustainability: OR=1.0031, 95% CI=1.0003-1.002), and replies (baseline: OR=1.02, 95% CI=1.005-1.04 and sustainability: OR=1.02, 95% CI=1.004-1.03). At the network level, users with the least followers, tweets, retweets, and replies had the greatest reach.

Conclusions:

Reach was reduced by time. Factors increasing reach at the user level had different impact at the network level. More than one strategy is needed to maximize reach.


 Citation

Please cite as:

El Tantawi M, Al-Ansari A, AlSubaie A, Fathy A, Aly NM, Mohamed AS

Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure

J Med Internet Res 2018;20(9):e10781

DOI: 10.2196/10781

PMID: 30213781

PMCID: 6231799

Per the author's request the PDF is not available.