Beth L Hoffman1, Jason B Colditz2, Ariel Shensa3, Riley Wolynn4, Sanya Bathla Taneja5, Elizabeth M Felter6, Todd Wolynn7, Jaime E Sidani8. 1. Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, 130 De Sotto Street, Pittsburgh, PA 15261, United States; Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: beth.hoffman@pitt.edu. 2. Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: colditzjb@pitt.edu. 3. Department of Physical Therapy, University of Pittsburgh School of Health and Rehabilitation Sciences, 4028 Forbes Tower, Pittsburgh, PA 15260, United States. Electronic address: ariel.shensa@pitt.edu. 4. Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: RIW22@pitt.edu. 5. Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: SBT12@pitt.edu. 6. Department of Behavioral and Community Health Sciences, Graduate School of Public Health, University of Pittsburgh, 130 De Sotto Street, Pittsburgh, PA 15261, United States. Electronic address: emfelter@pitt.edu. 7. Kids Plus Pediatrics, 4070 Beechwood Blvd, Pittsburgh, PA 15217, United States. Electronic address: todd@kidspluspgh.com. 8. Division of General Internal Medicine, Department of Medicine, University of Pittsburgh, School of Medicine, 1218 Scaife Hall, 35505 Terrace Street, Pittsburgh, PA 15261, United States; Center for Behavioral Health, Media, and Technology, University of Pittsburgh, School of Medicine, 230 McKee Place, Suite 600, Pittsburgh, PA 15213, United States. Electronic address: jaime.sidani@pitt.edu.
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.
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.
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