Sear Richard F, Velasquez Nicolas, Leahy Rhys, Restrepo Nicholas Johnson, Oud Sara El, Gabriel Nicholas, Lupu Yonatan, Johnson Neil F
Department of Computer ScienceGeorge Washington UniversityWashingtonDC20052USA.
Institute for Data, Democracy, and Politics, George Washington UniversityWashingtonDC20052USA.
IEEE Access. 2020 May 11;8:91886-91893. doi: 10.1109/ACCESS.2020.2993967. eCollection 2020.
A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations ("anti-vax"). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination ("pro-vax") community. However, the anti-vax community exhibits a broader range of "flavors" of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.
大量潜在危险的新冠疫情错误信息正在网上出现。在此，我们运用机器学习对那些反对权威健康指导（尤其是疫苗接种，即“反疫苗接种者”）的网络群体中的新冠疫情相关内容进行量化分析。我们发现，与支持疫苗接种的群体（“亲疫苗接种者”）相比，反疫苗接种群体围绕新冠疫情展开的辩论焦点较少。然而，反疫苗接种群体所讨论的新冠疫情话题“类型”更为广泛，因此能够吸引更广泛的在网上寻求新冠疫情相关指导的人群，例如那些对强制快速接种新冠疫苗持谨慎态度的人，或者那些寻求替代疗法的人。因此，相较于亲疫苗接种群体，反疫苗接种群体未来似乎更有能力吸引新的支持者。这令人担忧，因为广泛缺乏对新冠疫苗的接种将意味着全球无法实现群体免疫，使各国容易遭受未来新冠疫情的卷土重来。我们提供了一个机制模型来解释这些结果，并有助于评估干预策略可能产生的效果。我们的方法具有可扩展性，因此能够解决社交媒体平台面临的紧迫问题，即必须分析大量的在线健康错误信息和虚假信息。
