Weaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate

Objectives. To understand how Twitter bots and trolls (“bots”) promote online health content. Methods. We compared bots’ to average users’ rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing...

Full description

Saved in:
Bibliographic Details
Published in:American journal of public health (1971) Vol. 108; no. 10; pp. 1378 - 1384
Main Authors: Broniatowski, David A., Jamison, Amelia M., Qi, SiHua, AlKulaib, Lulwah, Chen, Tao, Benton, Adrian, Quinn, Sandra C., Dredze, Mark
Format: Journal Article
Language:English
Published: United States American Public Health Association 01.10.2018
Subjects:
AJPH Open-Themed Research
Application programming interface
Automation
Business communications
Computer mediated communication
Content analysis
Data collection
Debates
Dictionaries
False information
Health Communication
Health education
Health information
Humans
Hypotheses
Immunization
Immunization/Vaccines
Information dissemination
Internet
Malware
Measles
Media
Messages
Mortality
Political communication
Pollution
Public Health
Russia
Social Media
Social networks
Software agents
Vaccination
Vaccination - psychology
Vaccine hesitancy
Vaccines
World Wide Web
Russia
ISSN:0090-0036, 1541-0048, 1541-0048
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objectives. To understand how Twitter bots and trolls (“bots”) promote online health content. Methods. We compared bots’ to average users’ rates of vaccine-relevant messages, which we collected online from July 2014 through September 2017. We estimated the likelihood that users were bots, comparing proportions of polarized and antivaccine tweets across user types. We conducted a content analysis of a Twitter hashtag associated with Russian troll activity. Results. Compared with average users, Russian trolls (χ 2 (1) = 102.0; P < .001), sophisticated bots (χ 2 (1) = 28.6; P < .001), and “content polluters” (χ 2 (1) = 7.0; P < .001) tweeted about vaccination at higher rates. Whereas content polluters posted more antivaccine content (χ 2 (1) = 11.18; P < .001), Russian trolls amplified both sides. Unidentifiable accounts were more polarized (χ 2 (1) = 12.1; P < .001) and antivaccine (χ 2 (1) = 35.9; P < .001). Analysis of the Russian troll hashtag showed that its messages were more political and divisive. Conclusions. Whereas bots that spread malware and unsolicited content disseminated antivaccine messages, Russian trolls promoted discord. Accounts masquerading as legitimate users create false equivalency, eroding public consensus on vaccination. Public Health Implications. Directly confronting vaccine skeptics enables bots to legitimize the vaccine debate. More research is needed to determine how best to combat bot-driven content.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
Peer Reviewed
CONTRIBUTORS
D. A. Broniatowski designed the study, conducted the statistical analyses, and wrote the first draft of the article. A. M. Jamison conducted the qualitative analysis. A. M. Jamison, S. Qi, and L. Alkulaib conducted the sentiment coding. A. M. Jamison, S. Qi, L. Alkulaib, T. Chen, A. Benton, S. C. Quinn, and M. Dredze critically revised the article. S. Qi, L. Alkulaib, T. Chen, and A. Benton collected and analyzed Twitter data. S. C. Quinn and M. Dredze assisted with study design.
ISSN:0090-0036
1541-0048
1541-0048
DOI:10.2105/AJPH.2018.304567