I Tweet, Therefore I Learn: An Analysis of Twitter Use Across Anesthesiology Conferences

BACKGROUND: Twitter in anesthesiology conferences promotes rapid science dissemination, global audience participation, and real-time updates of simultaneous sessions. We designed this study to determine if an association exists between conference attendance/registration and 4 defined Twitter metrics. METHODS: Using publicly available data through the Symplur Healthcare Hashtags Project and the Symplur Signals, we collected data on total tweets, impressions, retweets, and replies as 4 primary outcome metrics for all registered anesthesiology conferences occurring from May 1, 2016 to April 30, 2017. The number of Twitter participants, defined as users who contributed a tweet, retweet, or reply 3 days before through 3 days after the conference, was collected. We also collected influencer data as determined by mentions (number of times a user is referenced). Two authors independently verified the categories for influencers assigned by Symplur. Conference demographic data were obtained by e-mail inquiries. Associations between meeting attendees/registrants and Twitter metrics, between Twitter participants and the metrics, and between physician influencers and Twitter participants were tested using Spearman rho. RESULTS: Fourteen conferences with 63,180 tweets were included. With the American Society of Anesthesiologists annual meeting included, the correlations between meeting attendance/registration and total tweets (r(s) = 0.588; P = .074), impressions (r(s) = 0.527; P = .117), and retweets (r(s) = 0.539; P = .108) were not statistically significant; for replies, it was moderately positive (r(s) = 0.648; P = .043). Without the American Society of Anesthesiologists annual meeting, total tweets (r(s) = 0.433; P = .244), impressions (r(s) = 0.350; P = .356), retweets (r(s) = 0.367; P = .332), and replies (r(s) = 0.517; P = .154) were not statistically significant. Secondary outcomes include a highly positive correlation between Twitter participation and total tweets (r(s) = 0.855; P < .001), very highly positive correlations between Twitter participation and impressions (r(s) = 0.938; P < .001), retweets (r(s) = 0.925; P < .001), and a moderately positive correlation between Twitter participation and replies (r(s) = 0.652; P = .044). Doctors were top influencers in 8 of 14 conferences, and the number of physician influencers in the top 10 influencers list at each conference had a moderately positive correlation with Twitter participation (r(s) = 0.602; P = .023). CONCLUSIONS: We observed that the number of Twitter participants for a conference is positively associated with Twitter activity metrics. No relationship between conference size and Twitter metrics was observed. Physician influencers may be an important driver of participants.