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      Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017–2019

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          Abstract

          Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.

          Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017–December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.

          Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168–4435), of which 27 (IQR = 6–169) were vaccine critical, and 0 (IQR = 0–12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).

          Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.

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          Most cited references18

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          Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.

          Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300-700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.
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            Addressing Health-Related Misinformation on Social Media

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              Is Open Access

              Facebook language predicts depression in medical records

              Significance Depression is disabling and treatable, but underdiagnosed. In this study, we show that the content shared by consenting users on Facebook can predict a future occurrence of depression in their medical records. Language predictive of depression includes references to typical symptoms, including sadness, loneliness, hostility, rumination, and increased self-reference. This study suggests that an analysis of social media data could be used to screen consenting individuals for depression. Further, social media content may point clinicians to specific symptoms of depression.
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                Author and article information

                Journal
                American Journal of Public Health
                Am J Public Health
                American Public Health Association
                0090-0036
                1541-0048
                October 2020
                October 2020
                : 110
                : S3
                : S319-S325
                Affiliations
                [1 ]Adam G. Dunn and Jason Dalmazzo are with the Discipline of Biomedical Informatics and Digital Health, University of Sydney, Sydney, Australia. Didi Surian, Maryke Steffens, Amalie Dyda, and Enrico Coiera are with the Centre for Health Informatics, Macquarie University, Sydney, Australia. Dana Rezazadegan is with the Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, Australia. Julie Leask is with the Susan Wakil School of Nursing and Midwifery,...
                Article
                10.2105/AJPH.2020.305902
                7532316
                33001719
                48dda88b-1696-4643-9021-8c034e8ccc7f
                © 2020
                History

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