RAPID: Automated Extraction and Validation of the Gist of Social Media Messages about COVID-19

当新型冠状病毒席卷地球仪时，民主社会面临着一个困境，即如何鼓励牺牲以降低风险，即使威胁是无形的，似乎正在消退。当风险很高时，个人可能需要长时间保持极端形式的社交距离，但这种选择会带来经济和个人成本。然而，不参与这些减少风险的活动可能会导致数百万人死亡。因此，公共卫生传播者必须理解拒绝这些行动的理由，并将风险传播建立在经验支持的原则基础上，包括需要传达要点（底线意义）这些行动的个人作出这些具有挑战性的选择。这个项目包括三个研究，每个有两个样本在不同的时间点，在随后的研究中，最终目标是将社交媒体信息的要点转化为有效的风险沟通干预措施。该方法的动机是模糊痕迹理论的证据为基础的帐户下的健康决策的风险，它假定的决定是基于定性的要点表示的刺激，编码的基本含义的上下文中。学者们使用自下而上的无监督机器学习算法来测试时间敏感的假设，以描述社交媒体信息中关于COVID-19的主题，以及自上而下的人类对理论上可预测的心理表征的判断，这些心理表征是感知风险和降低风险行为的益处的要点。研究1量化了与社交距离和其他降低风险行为（例如，通过从Twitter和公开的Facebook帖子中提取数百万条社交媒体消息，这涉及使用概率主题模型自动编码数百万社交媒体消息的持续语料库。研究2提供了一个系统的方法与人类法官解释的主题模型提取的主题在不同的时间点。研究3涉及一组不同的人类法官，他们评估主题与一组有针对性的风险和收益的要点表示的一致性，沿着表达价值的要点原则。研究人员改编了先前研究中成功使用的项目。类似的要点表示预测了许多健康状况的自我报告的风险降低行为。因此，在研究3中，研究者通过一个理论动机的调查来验证主题要点，并分析其信度和结构效度。重点是对风险和益处的要点的看法，特别是可以促进个人和社会健康的要点。总的来说，该项目引入了从社交媒体中吸引专家的新技术，这些技术可用于生成有意义的公共卫生传播。该项目直接告知公共卫生传播者的现有尝试，以表达人口成员为减缓COVID-19大流行病传播而应采取的风险、益处和行动。这些措施针对人类用户进行验证，使团队能够实现准确性和规模。这个项目作为一个更大的努力，可以提高的程度，其中要点启发可能是自动化的基础，帮助公共卫生传播者，以快速了解哪些要点应传达给哪些社区在爆发期间。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

While the novel coronavirus sweeps the globe, democratic societies face a quandary, namely, how to encourage sacrifices to reduce risk, even when the threat is invisible and seems to be receding. When risk is high, individuals may need to engage in extreme forms of social distancing for long periods, but this choice comes with economic and personal costs. Not engaging in these risk-reduction activities, however, could cause millions of deaths. It is therefore crucial for public health communicators to understand rationales for refusing these actions and to base risk communication on empirically supported principles, including the need to convey the gist (bottom-line meaning) of these actions to individuals making these challenging choices.This project consists of three studies, each with two samples at different time points, with the ultimate goal in subsequent studies of converting the gists of social media messages into effective risk-communication interventions. The approach is motivated by Fuzzy Trace Theory–an evidence-based account of health decision making under risk–which posits that decisions are based on qualitative gist representations of stimuli that encode basic meaning in context. The scholars test time-sensitive hypotheses using bottom-up unsupervised machine-learning algorithms to characterize the topics in social media messages about COVID-19 and top-down human judgments about theoretically predictable mental representations of the gist of perceived risks and benefits of risk-reduction behaviors. Study 1 quantifies the prevalence of gists pertaining to social distancing and other risk-reduction behaviors (e.g., hand washing) by extracting millions of social media messages from Twitter and public Facebook posts. This involves automatic coding an ongoing corpus of millions of social media messages using probabilistic topic models. Study 2 provides a systematic approach with human judges to interpreting the topics extracted by a topic model at different time points. Study 3 involves a different group of human judges assessing topics for their consistency with a targeted set of gist representations of risks and benefits, along with gist principles that express values. The researchers adapt items used successfully in prior research. Similar gist representations have predicted self-reported risk-reduction behaviors for numerous health conditions. Thus, for Study 3, the researchers validate topic gists by fielding a theoretically motivated survey analyzed for reliability and construct validity. The focus is on perceptions of the gist of risks and benefits, especially gists that can promote the health of individuals and society. Overall, this project introduces novel techniques for eliciting gists from social media that can be used to generate meaningful public health communications. The project directly informs existing attempts by public health communicators to express the risks, benefits, and actions that members of the population should take to mitigate the spread of the COVID-19 pandemic. The measures are validated against human users, enabling the team to achieve both accuracy and scale. This project serves as the basis for a larger effort that can increase the extent to which gist elicitation may be automated, helping public health communicators to quickly understand which gists should be communicated to which communities during outbreaks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.