RI: CAREER : Understanding Opinions by Reasoning over Socially Grounded Language

社交媒体平台最近已经成为公共对话的主要空间，为人们分享观点和政策制定者宣传他们的决定并告知公众提供了一个场所。海量的意见数据为研究这些平台上表达的观点提供了诱人的机会。从这一分析中得出的见解可以帮助衡量公众舆论，为公共政策提供信息，并帮助支持人类的决策。要实现这些机会，需要适应新的社交媒体环境的模式，在新的社交媒体环境中，语言内容及其社会背景不可分离。这个职业项目开发了新的建模技术和学习算法，以便在共同的创新原则下将这两个方面结合起来--创建一种基于社会的语言表示法，将意见理解视为理解现实世界情景(如实施特定政策或对紧急情况的反应)及其参与者的更大框架的一部分。这项研究有助于提供更好地理解社交媒体内容所需的相关背景，并产生高度细微的分析，捕捉给定现实世界场景中不同利益相关者之间的立场、态度和关系。该项目提出了一种新的方法，将固执己见的文本分析概念化，作为现实世界场景的一部分，反映文本产生场景中利益相关者的态度和关系。一个主要的设计目标是避免监督瓶颈，并通过使用与用户相关的社会信息作为对他们编写的文档的间接监督形式，使系统能够轻松适应新的事件和政策问题。这是通过在共享的神经符号框架中表示文档、作者、被引用实体、它们的联系和行为来完成的，该框架允许对从数据学习的潜在实体表示进行符号推理。该项目解决了三个主要挑战：(1)构建一种表征意见、其目标和动机以及他们所表达的立场的表示语言，(2)通过将相关的真实世界信息注入神经语言模型来将意见文本植根于现实世界情景中，以及(3)通过形成社会、行为和文本信息的统一视图来利用社会信息。这些研究工作有助于从本身缺乏特殊性的社交媒体内容中提供细致入微的见解，同时为联合处理文本和社交信息建立计算基础。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。

Social media platforms have recently emerged as the primary space for public conversations, providing a venue for people to share perspectives and for policymakers to promote their decisions and inform the public about them. The massive amount of available opinion data presents tantalizing opportunities to study the perspectives expressed on these platforms. Insights derived from this analysis can help gauge public opinion, inform public policy, and help support human decision making. Realizing these opportunities requires models adapted to the new social media settings, in which linguistic content and its social context cannot be separated. This CAREER project develops novel modeling techniques and learning algorithms for combining these two aspects under a common innovative principle -- creating a socially grounded language representation that views opinion understanding as part of a larger framework of understanding real-world scenarios (such as the implementation of specific policies or the response to an emergency situation) and their participants. This research helps provide the relevant context needed for better understanding social media content and result in highly nuanced analysis, capturing the stances, attitudes and relationships between the different stakeholders of a given real-world scenario.This project suggests a new way to conceptualize opinionated text analysis, as part of a real-world scenario, reflecting the attitudes-of and relationships-between stakeholders in the scenario from which the text emerges. A major design goal is to avoid the supervision bottleneck, and allow the system to easily adapt to new events and policy issues by using the social information associated with users as a form of indirect supervision over documents they author. This is done by representing documents, authors, referenced entities, their connections and behaviors in a shared neuro-symbolic framework enabling symbolic inference over latent entity representations learned from data. The project addresses three main challenges: (1) constructing a representation language for characterizing opinions, their targets and motivation, and the stances they express, (2) grounding opinion text in real world scenarios by infusing relevant real-world information into a neural language model, and (3) exploiting social information by formulating a unified view of social, behavioral, and textual information. These research efforts help provide nuanced insights from social media content that lacks specificity on its own, while building the computational foundations for jointly processing textual and social information.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.