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

当新型冠状病毒席卷全球时，民主社会面临着一个进退两难的境地，即如何鼓励牺牲以降低风险，即使威胁是看不见的，似乎正在消退。当风险很高时，个人可能需要长时间从事极端形式的社交疏远，但这种选择伴随着经济和个人成本。然而，如果不参与这些降低风险的活动，可能会导致数百万人死亡。因此，对于公共卫生传播者来说，理解拒绝这些行动的理由并将风险沟通建立在经验支持的原则上是至关重要的，包括需要向做出这些具有挑战性的选择的个人传达这些行动的要点(底线意义)。该项目由三项研究组成，每项研究在不同的时间点都有两个样本，最终目标是在后续研究中将社交媒体信息的要点转化为有效的风险沟通干预措施。这种方法是由模糊痕迹理论推动的--一种基于证据的风险下健康决策的描述--该理论认为，决策是基于对刺激的定性主旨表示，这些刺激在上下文中编码基本含义。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型假设，以表征有关新冠肺炎的社交媒体消息中的主题，以及自上而下的人类对感知到的风险和降低风险行为的好处的心理表征在理论上可以预测的判断。研究1通过从Twitter和Facebook公开帖子中提取数百万条社交媒体消息，量化了与社交距离和其他降低风险行为(例如洗手)有关的咨询师的流行率。这涉及到使用概率主题模型对数百万条社交媒体消息的持续语料库进行自动编码。研究2提供了一种系统的方法，由人类法官来解释主题模型在不同时间点提取的主题。研究3涉及一组不同的人类评委，他们评估主题与一组有针对性的风险和利益的主要表现，以及表达价值观的基本原则的一致性。研究人员对先前研究中成功使用的项目进行了调整。类似的要点陈述预测了许多健康状况下自我报告的降低风险的行为。因此，在研究3中，研究人员通过对信度和结构效度进行分析的理论动机的调查来验证话题引导者。重点是对风险和收益的主旨的看法，特别是能够促进个人和社会健康的科学家。总体而言，该项目引入了从社交媒体中引出专家的新技术，这些技术可用于产生有意义的公共卫生传播。该项目直接告知公共卫生传播者现有的尝试，以表达人口成员为减轻新冠肺炎大流行的传播而应采取的风险、好处和行动。这些措施针对人类用户进行了验证，使团队能够同时实现准确性和规模。该项目是一个更大的努力的基础，该努力可以增加GIST启发的自动化程度，帮助公共卫生传播者快速了解在疫情爆发期间应该将哪些指导者传达给哪些社区。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.