Social-Cyber Maneuvers During the COVID-19 Vaccine Initial Rollout : Content Analysis of Tweets

©Janice T Blane, Daniele Bellutta, Kathleen M Carley. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.03.2022..

BACKGROUND: During the time surrounding the approval and initial distribution of Pfizer-BioNTech's COVID-19 vaccine, large numbers of social media users took to using their platforms to voice opinions on the vaccine. They formed pro- and anti-vaccination groups toward the purpose of influencing behaviors to vaccinate or not to vaccinate. The methods of persuasion and manipulation for convincing audiences online can be characterized under a framework for social-cyber maneuvers known as the BEND maneuvers. Previous studies have been conducted on the spread of COVID-19 vaccine disinformation. However, these previous studies lacked comparative analyses over time on both community stances and the competing techniques of manipulating both the narrative and network structure to persuade target audiences.

OBJECTIVE: This study aimed to understand community response to vaccination by dividing Twitter data from the initial Pfizer-BioNTech COVID-19 vaccine rollout into pro-vaccine and anti-vaccine stances, identifying key actors and groups, and evaluating how the different communities use social-cyber maneuvers, or BEND maneuvers, to influence their target audiences and the network as a whole.

METHODS: COVID-19 Twitter vaccine data were collected using the Twitter application programming interface (API) for 1-week periods before, during, and 6 weeks after the initial Pfizer-BioNTech rollout (December 2020 to January 2021). Bot identifications and linguistic cues were derived for users and tweets, respectively, to use as metrics for evaluating social-cyber maneuvers. Organization Risk Analyzer (ORA)-PRO software was then used to separate the vaccine data into pro-vaccine and anti-vaccine communities and to facilitate identification of key actors, groups, and BEND maneuvers for a comparative analysis between each community and the entire network.

RESULTS: Both the pro-vaccine and anti-vaccine communities used combinations of the 16 BEND maneuvers to persuade their target audiences of their particular stances. Our analysis showed how each side attempted to build its own community while simultaneously narrowing and neglecting the opposing community. Pro-vaccine users primarily used positive maneuvers such as excite and explain messages to encourage vaccination and backed leaders within their group. In contrast, anti-vaccine users relied on negative maneuvers to dismay and distort messages with narratives on side effects and death and attempted to neutralize the effectiveness of the leaders within the pro-vaccine community. Furthermore, nuking through platform policies showed to be effective in reducing the size of the anti-vaccine online community and the quantity of anti-vaccine messages.

CONCLUSIONS: Social media continues to be a domain for manipulating beliefs and ideas. These conversations can ultimately lead to real-world actions such as to vaccinate or not to vaccinate against COVID-19. Moreover, social media policies should be further explored as an effective means for curbing disinformation and misinformation online.

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:24

Enthalten in:

Journal of medical Internet research - 24(2022), 3 vom: 07. März, Seite e34040

Sprache:

Englisch

Beteiligte Personen:

Blane, Janice T [VerfasserIn]
Bellutta, Daniele [VerfasserIn]
Carley, Kathleen M [VerfasserIn]

Links:

Volltext

Themen:

Anti-vaccine
BEND maneuvers
BNT162 Vaccine
Belief
COVID-19
COVID-19 Vaccines
Communication
Community
Coronavirus
Cybersecurity
Disinformation
Health information
Journal Article
Manipulation
N38TVC63NU
ORA-PRO
Pro-vaccine
Research Support, Non-U.S. Gov't
Security
Social cybersecurity
Social media
Social network analysis
Social-cyber maneuvers
Twitter
Vaccine

Anmerkungen:

Date Completed 10.03.2022

Date Revised 16.07.2022

published: Electronic

Citation Status MEDLINE

doi:

10.2196/34040

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM335790135