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

在社交计算平台中，每个用户都是潜在的信息源，这些平台已经彻底改变了内容在多个领域的生成和传播方式，例如新闻、医疗保健和在线教育。在这些开放的环境中，用户经常在不知道信息的来源和专业水平的情况下访问信息，这使他们更容易受到操纵，更容易接触到有偏见的甚至欺骗性的内容。该项目旨在帮助用户浏览信息空间，通过定义和实施信息污染的概念，信息污染是指信息供应被无关、多余、未经请求、不正确和其他低价值的信息污染，并提出通过扩大相关背景来消除其不利影响的原则性方法。该框架将确定围绕公共利益主题可能存在的各种视角，确定相关的专门知识，从而改善公众获得各种和值得信赖的信息的机会。该项目的方法将问题分解为其核心组成部分-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及其支持证据，到理解信息来源、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。调查人员将定义新的学习和推理任务，这些任务将为解决信息污染问题提供重要的基础。其中包括一个综合框架，通过合并关于来自多个文件的来源及其与其他来源的互动的信息来评估信息的可信性。此外，该项目将确定新的语言理解任务，这些任务提供新的抽象，支持描述索赔之间的相似和不同之处、索赔背后的意图、索赔所表达的观点及其影响。研究目标将得到一个全面的评估计划的补充，该计划包括对每个能力的内在评估，以及衡量整个系统将对用户消费的信息产生的影响的外部评估。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 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.