TY  - JOUR
AU  - El Tantawi, Maha
AU  - Al-Ansari, Asim
AU  - AlSubaie, Abdulelah
AU  - Fathy, Amr
AU  - Aly, Nourhan M
AU  - Mohamed, Amira S
PY  - 2018
DA  - 2018/09/13
TI  - Reach of Messages in a Dental Twitter Network: Cohort Study Examining User Popularity, Communication Pattern, and Network Structure
JO  - J Med Internet Res
SP  - e10781
VL  - 20
IS  - 9
KW  - social media
KW  - health communication
KW  - dentists
KW  - students, dental
KW  - social network analysis
KW  - twitter
KW  - social networks
AB  - 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. 
SN  - 1438-8871
UR  - http://www.jmir.org/2018/9/e10781/
UR  - https://doi.org/10.2196/10781
UR  - http://www.ncbi.nlm.nih.gov/pubmed/30213781
DO  - 10.2196/10781
ID  - info:doi/10.2196/10781
ER  -