#DoctorsSpeakUp: Lessons learned from a pro-vaccine Twitter event. Issue 19 (6th May 2021)

• Record Type: Journal Article
• Authors: Hoffman, Beth L.; Colditz, Jason B.; Shensa, Ariel; Wolynn, Riley; Taneja, Sanya Bathla; Felter, Elizabeth M.; Wolynn, Todd; Sidani, Jaime E.
• Is Part Of: Vaccine. Volume 39:Issue 19(2021)
• Page Start: 2684
• Page End: 2691
• Publication Date: 2021-05-06
• Subjects: Twitter -- Social media -- Anti-vaccine -- Health communication; Vaccines -- Periodicals; 615.372
• DOI: 10.1016/j.vaccine.2021.03.061
• Languages: English
• ISSNs: 0264-410X

Abstract

Highlights: Insight on Tweets from the pro-vaccine #DoctorsSpeakUp event. Anti-vaccine movement mobilized prior to the event. Pro-vaccine messages often did not follow risk communication guidelines. Findings offer concrete recommendations for future pro-vaccine events. Abstract: Background: In response to growing anti-vaccine activism on social media, the #DoctorsSpeakUp event was designed to promote pro-vaccine advocacy. This study aimed to analyze Twitter content related to the event to determine (1) characteristics of the Twitter users who authored these tweets, (2) the proportion of tweets expressing pro-vaccine compared to anti-vaccine sentiment, and (3) the content of these tweets. Methods: Data were collected using Twitter's Filtered Streams Interface, and included all publicly available tweets with the "#DoctorsSpeakUp" hashtag on March 5, 2020, the day of the event. Two independent coders assessed a 5% subsample of original tweets (n = 966) using a thematic content analysis approach. Cohen's κ ranged 0.71–1.00 for all categories. Chi-square and Fisher's exact tests were used to examine associations between tweet sentiment, type of account, and tweet content (personal narrative and/or statement about research or science). Accounts were analyzed for likelihood of being a bot (i.e. automated account) using Botometer. Results: Of 847 (87.7%) relevant tweets, 244 (28.8%) were authored by a Twitter user that identified as a parent and 68 (8.0%) by a user that identified as a health professional. With regard to sentiment, 167 (19.7%) were coded as pro-vaccine and 668 (78.9%) were coded as anti-vaccine. Tweet sentiment was significantly associated with type of account (p < 0.001) and tweet content (p = 0.001). Of the 575 unique users in our dataset, 31 (5.4%) were classified as bots using Botometer. Conclusions: Our results suggest a highly coordinated response of devoted anti-vaccine antagonists in response to the #DoctorsSpeakUp event. These findings can be used to help vaccine advocates leverage social media more effectively to promote vaccines. Specifically, it would be valuable to ensure that pro-vaccine messages consider hashtag use and pre-develop messages that can be launched and promoted by pro-vaccine advocates.