III: Small: Mirador: Explainable Computational Models for Recognizing and Understanding Controversial Topics Encountered Online

该项目旨在开发算法和工具，使人们能够识别网页或其他文档讨论的一个或多个有争议的主题-也就是说，在一些相当大的人群中存在强烈的分歧。该项目将开发算法和工具来解释围绕该主题的争议，确定不同意的人群，他们采取的立场，以及这些立场如何相互冲突。这些算法的进步将扩大研究界对如何对主题的讨论和分歧进行计算建模以及如何将结果信息传达给一般用户的理解。该项目将帮助人们对在线材料进行批判性评估，并帮助他们理解为什么一个页面是有教育意义的，或者为什么它不是。这个项目的目的是为用户提供一些工具，这些工具可以阐明某个人找到的主题或单个页面或文档的主题的更广泛的上下文。先前的研究表明，可以合理准确地识别出一份文件是有争议话题的一部分，但这种工作在不同的流派中是碎片化的，需要更强大的建模和更彻底的评估，并且缺乏解释力，无法帮助读者理解文本为什么以及如何引起争议。在这个项目中，研究人员探索了一些基本问题，即如何通过计算对争议进行建模，以便“在野外”识别争议。该项目还探索了模型的变化，允许算法提取对争议本质的解释。该项目应用并扩展了文本分析和比较技术。它利用强大的统计语言建模方法以及最近的神经网络（深度学习）方法来表示文本、文本的争议性、立场及其关系，所有这些都是从Web页面和其他文档中提取的。该模型最初将用于离线识别已知有争议的主题集合，然后通过监测缓慢变化的新闻来源和博客帖子以及短暂的微博数据源来调整该集合，以捕捉争议的快速变化。研究人员将通过提供一个开源示例服务器来提供所得到的技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to develop algorithms and tools that allow a person to recognize that a web page or other document discusses one or more topics that are controversial -- that is, about which there is strong disagreement within some sizeable group of people. The project will develop algorithms and tools that explain the controversy surrounding the topic, identifying the populations that disagree, the stances that they take, and how those stances conflict with each other. The advances in these algorithms will broaden the research community's understanding of how discussions and disagreements on topics can be modeled computationally and how that resulting information can be conveyed to a general user. The project will assist people in critical evaluation of on-line material and help them understand why a page is educative or why it is not. The aim of this project is to provide users with tools that illuminate the broader context of the topic or topics of a single page or document that someone finds. Previous work has shown that it is possible to recognize with reasonable accuracy that a document is part of a controversial topic, but that work is fragmented across different genres, demands more robust modeling and more thorough evaluation, and lacks explanatory power that can help a reader understand why and how a text is contentious. In this project, the researchers explore fundamental questions about how controversy can be modeled computationally so that it can be recognized "in the wild". The project also explores model variations that allow an algorithm to extract an explanation of the nature of the controversy. The project applies and extends text analysis and comparison techniques. It leverages powerful statistical language modeling methods as well as recent neural network (deep learning) approaches to represent text, its controversial nature, its stances, and their relationships, all extracted from Web pages and other documents. The modeling will be initially used offline to identify collections of topics known to be controversial and then adapt that collection by monitoring slowly-changing news sources and blog postings as well as ephemeral microblog sources of data to capture rapid changes in controversy. The researchers will make the resulting techniques available by providing an open-source example server.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.