Collaborative Research: III: Small: Robust Learning and Inference Protocols for Mitigating Information Pollution

每个用户都是潜在信息源的社会计算平台已经彻底改变了在多个领域生成和传播内容的方式，例如新闻、医疗保健和在线教育等。在这些开放的环境中，用户往往在不了解信息来源和专业知识水平的情况下获取信息，这使他们更容易受到操纵，更容易接触到有偏见甚至欺骗性的内容。该项目旨在通过定义和操作信息污染的概念来帮助用户导航信息空间，信息污染是指不相关的、冗余的、未经请求的、不正确的和其他低价值的信息对信息供应的污染，并建议通过增加相关背景来对抗其不利影响的原则方法。该框架将确定围绕公共利益主题可能存在的观点范围，确定相关专业知识，从而改善公众获取多样化和可信赖信息的途径。该项目的目标是解决一些关键研究问题，以支持减轻信息污染。该项目的方法将问题分解为其核心组件-从试图识别和呈现索赔可能具有的多个角度时出现的关键自然语言处理问题，以及其支持证据，到理解信息源，他们提出的索赔和他们提供的证据，再到可信度的算法推理框架。研究人员将定义新的学习和推理任务，为解决信息污染问题提供重要的构建模块。其中包括一个整体框架，通过整合来自多个文档的来源信息及其与其他来源的交互来评估信息的可信度。此外，该项目将定义新的语言理解任务，这些任务将提供新的抽象，以支持对不同主张之间的异同、它们背后的意图、它们所表达的观点及其含义的描述。研究目标将由一个综合的评估计划来补充，该计划既包括对每种能力的内在评估，也包括衡量整个系统对用户消费信息的影响的外在评估。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。

Social computing platforms, in which every user is a potential information source, have revolutionized the way content is generated and disseminated in multiple fields, such as news, healthcare and online education, to name a few. In these open settings, users often access information without awareness of its source and its level of expertise making them more susceptible to manipulation and exposure to biased or even deceptive content. This project is designed to help users navigate the information space, by defining and operationalizing the concept of Information Pollution, the contamination of information supply with irrelevant, redundant, unsolicited, incorrect, and otherwise low-value information, and suggest principled methods for combating its adverse effects by augmenting it with the relevant context. The framework will identify the spectrum of perspectives that could exist around topics of public interest, identify relevant expertise, and thus improve public access to diverse and trustworthy informationThe goal of this project is to address some of the key research questions in support of mitigating information pollution. The project’s approach decomposes the problem into its core components – from key natural language processing problems that arise when attempting to identify and present the multiple perspectives a claim might have, along with its supporting evidence, to understanding information sources, the claims they make and evidence they provide, to an algorithmic inference framework for trustworthiness. The investigators will define novel learning and inference tasks that would provide important building blocks for addressing the information pollution problem. These include a holistic framework for assessing trustworthiness of information by consolidating information about sources from multiple documents and their interactions with other sources. In addition, the project will define novel language understanding tasks which provide new abstractions supporting the characterization of the similarities and differences between claims, the intent behind them, perspectives they express, and their implications. The research goals will be complemented by a comprehensive evaluation plan, consisting of both intrinsic evaluation of each capability, as well as extrinsic evaluation measuring the impact the full system will have on information consumed by users.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.