Collaborative Research: HCC: Medium: Fine-grained Emotion Analysis in Crises

历史上，同一事件被不同群体完全不同地解读的情况比比皆是。通过OJ辛普森案、新冠肺炎危机和乔治·弗洛伊德被谋杀等事件，我们观察到社区领导人、警察部门、政策制定者和普通公民对事件的不同反应。这个项目的目的是开始确定危机的社会、情感和语言标记(例如，社会动荡、自然灾害等)。预测人们对相同事件的不同反应方式。这是通过分析社交媒体的语言来实现的，社交媒体是一种快速增长的数据来源，我们可以从这些数据来源中了解情绪的表达和感知，从学术研究到公共政策都具有深远的潜在用途。理解情绪、围绕这些情绪的上下文以及随后的行为对于处于危机中的人、寻求有关危机的信息或帮助管理危机应对措施具有重要价值。该项目将发现跨不同社交平台提供全面、细粒度情感分析的机制，并得出稳健可靠的预测模型。细粒度情感分析的目的是：(1)检测文本中情感的表达并刻画其强度和极性；(2)识别引发情感的诱因；(3)分析情感偏离(即人们对同一触发因素表达的不同情感)。这项研究将贡献在社交媒体上跨不同危机表达的带注释的情绪数据集，以及配备了深度语言理解的可概括模型，用于情境化的情绪分析。行业和学术合作伙伴将通过评估模型在不同于开发模型的情况和数据源的能力来参与。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。

History is rich with situations where the same event has been interpreted completely differently by different groups of people. Through events such as the OJ Simpson case, the COVID-19 crisis, and the murder of George Floyd, we have observed disparate reactions to events that community leaders, police departments, policymakers, and everyday citizens fail to anticipate. The purpose of this project is to begin to identify social, emotional, and linguistic markers of crises (e.g., social turmoil, natural disasters, etc.) that predict the various ways people will react to the same events. This is achieved by analyzing the language of social media, a rapidly-growing source of data from which we can understand the expression and perception of emotions at a very large scale, with far-reaching potential uses from academic research to public policy.Understanding emotions, the context surrounding these emotions, and subsequent behaviors are of great value to those in a crisis, seeking information about a crisis, or helping manage responses to a crisis. This project will discover mechanisms to provide comprehensive, fine-grained emotion analysis across different social platforms, and derive robust and reliable predictive models. Fine-grained emotion analysis aims to: (1) detect expressions of emotions in a text and characterize their intensity and polarity, (2) identify the triggers causing the emotions, and (3) analyze emotion deviation (i.e., the varied emotions that people express towards the same trigger). This research will contribute annotated datasets of emotions expressed on social media across distinct crises and generalizable models equipped with deep linguistic understanding for contextualized emotion analysis. Industry and academic partners will participate by evaluating the ability of the models to work on situations and data sources different from those used to develop the models.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.