Title

#CDCGrandRounds and #VitalSigns: A Cross-Sectional Analysis of Twitter Data

Document Type

Presentation

Publication Date

8-15-2017

Abstract

Theoretical Background and Research Questions/Hypothesis: The CDC hosts monthly panel presentations with webcast titled ‘Grand Rounds’ since September 2009. CDC also publishes a monthly report known as Vital Signs. The CDC uses two respective hashtags #CDCGrandRounds and #VitalSigns respectively to promote their monthly event and report on Twitter. Our research question is to quantify the effect of attaching images or videos to #CDCGrandRounds or #VitalSigns tweets on their retweet frequency.

Methods: Data was retrieved via Twitter Search Application Programming Interface. All original tweets containing the hashtag #CDCGrandRounds dated from April 21, 2011 to October 25, 2016 were retrieved (n=6,966). All original tweets containing the hashtag #VitalSigns dated from March 19, 2013 to October 31, 2016 were retrieved (n=15,015). Each corpus was then sub-set into cycles (#CDCGrandRounds: n=58, #VitalSigns: n= 42). A #CDCGrandRounds cycle is defined as all tweets referring to the pre-specified topic for that particular cycle. A #VitalSigns cycle is defined as the first day of the publication release, which is the first Tuesday of each month, until the day before the next publication is released. Any irrelevant tweets were excluded. We manually coded the 30 tweets with the highest number of retweets for each cycle, as whether it contained a form of media (a still image or a video). Univariable negative binomial regression models were applied to compute the probability ratio of each cycle, with the outcome variable being the retweet frequency and the predictor variable being whether a tweet contains media.

Results: Of the 58 #CDCGrandRounds cycles, 29 were found to have statistically significant difference between tweets with and without media. Of these 29 cycles, one had a probability ratio (PR) estimate1 but3. Two cycles were outliers: “Preventing Suicide: A Comprehensive Public Health Approach” (September 2015) with PR = 36.353 (95% CI, 4.869 – 343.845, P1 and3. There were three outliers: “Prescription Painkiller Overdoses” (July 2, 2013) with PR = 33.514 (95% CI, 8.715, 133.357, P

Conclusions: The effect of attaching images or photos increasing retweet frequency varied across cycles for original tweets with #CDCGrandRounds and #VitalSigns. Images or photos may or may not increase retweet frequency.

Implications for Research and/or Practice: Future research is needed to determine the optimal choice of images or photos attached to a tweet to maximize the influence of public health messages.

Sponsorship/Conference/Institution

National Conference on Health Communication, Marketing, and Media (NCHCMM)

Location

Atlanta, GA

Share

COinS