COVID-19 (Mis)Information Exposure and Messaging Effects in the United Kingdom

抗击COVID-19大流行需要了解人们对该疾病的信息，哪些误解可能普遍存在，以及官员如何提高公众知识并鼓励保护公众健康的行为。首先，这项研究将衡量人们对COVID-19的信念和态度，提供一个全面的地图，既有准确的知识，也有误解，特别是那些由阴谋思维驱动的误解。其次，该研究将对受访者获取COVID-19相关信息的在线来源进行分类。这将使我们能够衡量哪些准确的信息来源能够接触到广泛的受众，哪些在线错误信息来源正在系统地扭曲人们的观点。第三，这项研究将测试寻求纠正错误信息的公共卫生信息是否真的能有效改变人们的信念。行为和调查数据将在多波全国代表性调查中收集，该调查衡量对COVID-19的错误和准确信念（包括对误解和阴谋论的信念）的流行程度以及对公共卫生当局建议的支持。为了评估对公共卫生官员提供的信息的反应，第二波调查将包括一个随机实验，评估卫生和医疗当局提供的信息的效果。最后，这项研究将通过分析答卷人提供的行为数据，结合使用人工编码和机器学习方法，衡量人们在线消费的关于这一大流行病的信息的质量。这些数据将有助于确定哪些群体最经常接触到有关COVID-19的不准确或不可信信息，这将有助于设计有效的干预措施。

Fighting the COVID-19 pandemic requires understanding what information people have about the disease, which misperceptions might be prevalent, and how officials can improve public knowledge and encourage behaviours that will protect public health. First, this study will measure beliefs and attitudes about COVID-19, providing a thorough map of accurate knowledge, but also of misperceptions, particularly those driven by conspiratorial thinking. Second, the study will catalogue the online sources from which our respondents get COVID-19-related information. This will allow us to gauge which accurate information sources reach a wide audience and which sources of online misinformation are systematically distorting people's views. Third, the study will test whether public health messages that seek to correct misinformation are actually effective in changing people's beliefs. Behavioural and survey data will be collected in a multi-wave nationally representative survey that measures both prevalence of false and accurate beliefs about COVID-19 (including beliefs in misperceptions and conspiracy theories) and support for recommendations from public health authorities. To evaluate responses to information from public health officials, the second survey wave will include a randomized experiment evaluating the effects of messaging from health and medical authorities. Finally, the study will measure the quality of the information people consume online about the pandemic by analysing the behavioural data provided by respondents using a combination of human-coded and machine learning approaches. These data will make it possible to identify which groups are most frequently exposed to inaccurate or untrustworthy information about COVID-19, which should aid in the design of effective interventions.