Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate
Open accessAbstract
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.
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Online trolls are attempting to polarize social media users during political discourse.
“Taken together, these data suggest that online trolls are attempting to polarize social media users during political discourse.”
From Troll and divide: the language of online polarization (2022) · cites this paper as [38]
Prior research has revealed that trolls are often found in the centroids of clusters of polarized topics, driving the partisan discourse on both ends.
“Prior research has revealed that when exploring the clusters of polarized topics, trolls are often found in the centroids of these clusters, driving the partisan discourse on both ends (37, 38, 55).”
From Troll and divide: the language of online polarization (2022) · cites this paper as [38]
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- Authors
- Broniatowski, D. A., Jamison, A. M., Qi, S., AlKulaib, L., Chen, T., Benton, A., Quinn, S. C., & Dredze, M.
- Affiliations
- George Washington University (US)
- ORCID
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 2018-10
- Volume
- 108
- Issue
- 10
- Pages
- 1378-1384
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- PMC
- PMC6137759
- OpenAlex
- W2888571776
- Semantic Scholar
- 8415274c8fb370cbab84ad82ab2f469786ddee72
- Cited by
- 1,149
- References
- 8
- Citations by year
- 2026: 42 · 2025: 91 · 2024: 93 · 2023: 146 · 2022: 166 · 2021: 272 · 2020: 223 · 2019: 103 · 2018: 13
- FWCI
- 347.53
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- American Journal of Public Health (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Johns Hopkins University
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM114771)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Health › Social Media in Health Education
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Keywords
- Twitter bots, vaccine debate, vaccine hesitancy, online health communication, political polarization, disinformation, bot detection, vaccine misinformation, social media
- MeSH terms
- Humans, Public Health, Russia, Vaccination, Health Communication, Social Media
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