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

每个用户都是潜在的信息源的社交计算平台已经彻底改变了在多个领域（例如新闻，医疗保健和在线教育）中生成和传播内容的方式。在这些开放的环境中，用户往往在不了解信息来源及其专业水平的情况下获取信息，使他们更容易受到操纵，并接触到有偏见甚至欺骗性的内容。该项目旨在帮助用户浏览信息空间，通过定义和操作信息污染的概念，用不相关的，多余的，未经请求的，不正确的，否则低价值的信息污染信息供应，并建议原则性的方法，通过增加相关的上下文来对抗其不利影响。该框架将确定围绕公共利益主题可能存在的观点，确定相关的专业知识，从而改善公众对多样化和可信信息的访问。该项目的目标是解决一些关键的研究问题，以支持减轻信息污染。该项目的方法将问题分解为核心组件-从试图识别和呈现索赔可能具有的多个视角时出现的关键自然语言处理问题，沿着其支持证据，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。其中包括一个综合框架，通过整合多个文件来源的信息及其与其他来源的互动，评估信息的可信度。此外，该项目将定义新的语言理解任务，这些任务提供新的抽象，支持对权利要求之间的相似性和差异性的表征，它们背后的意图，它们表达的观点及其含义。研究目标将由一个全面的评估计划来补充，包括对每种能力的内在评估，以及衡量整个系统对用户消费的信息的影响的外在评估。这个奖项反映了NSF的法定使命，并被认为是值得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来支持的。

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.