User-Based Stance Analysis for Mitigating the Impact of Social Bots on Measuring Public Opinion with Stance Detection in Twitter

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.