III: Small: Collaborative Research: Towards End-to-End Computer-Assisted Fact-Checking

III：小型：协作研究：走向端到端计算机辅助事实核查

• 批准号: 1718398
• 负责人: Jun Yang
• 金额: $ 17.85万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718398&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Towards

This project will develop ClaimBuster, an end-to-end system for computer-assisted fact-checking. This system will monitor live discourses, social media, and news to catch factual claims, detect matches with a curated repository of fact-checks from professionals, and deliver the matches instantly to readers and viewers. For various types of new claims not checked before, ClaimBuster will automatically check them against knowledge databases and report if they are truthful. For novel claims where humans must be brought into the loop, the system will provide algorithmic and computational tools to assist laypersons and professionals in understanding and vetting the claims. ClaimBuster, upon completion of the proposed work, is positioned to become the first-ever automated fact-checking system for use on a broad spectrum of factual claims. Its use will be expanded to verify claims in various types of narratives, discourses and documents such as sports news, legal documents, and financial reports. It can benefit a large base of potential users including consumers, publishers, corporate competitors, and legal professionals, among others. It directly benefits consumers by improving information accuracy and transparency. It helps news organizations speed their fact-checking process and also ensure the accuracy of their own news stories. Businesses can use ClaimBuster to identify falsehoods in their competitors' and their own reports and press releases. It also assists professionals such as lawyers in verifying documents.ClaimBuster will use database query, data mining, and natural language processing techniques to aid fact-checking. The detailed research tasks in this project will be as follows. (1) Investigate how to model factual claims and produce their internal representations. For this, the team will create taxonomies of claim templates in different domains, categorize claims based on the taxonomies, and generate internal representations through semantic parsing of the claims' textual forms. Such domain-specific modeling and internal representation of claims will enable novel methods and systematic, coherent solutions for other components of the system. (2) For algorithmic fact-checking, they will devise novel methods for translating claims into structured queries, keyword queries and natural language questions. Results of these queries over general and domain-specific databases and knowledge graphs will be compared with the answers embedded in the claims themselves, to verify if the claims check out. (3) By viewing claims as parameterized queries, they will develop methods based on perturbation analysis to find counter-arguments to claims and to find "interesting" factlets from datasets. These results will help ClaimBuster in identifying "cherry-picking" claims -- claims that are correct but misleading.

该项目将开发ClaimBuster，这是一个端到端的计算机辅助事实核查系统。该系统将监控实时话语，社交媒体和新闻，以捕捉事实主张，检测与专业人士事实检查库的匹配，并立即将匹配内容提供给读者和观众。对于之前没有检查过的各种类型的新索赔，ClaimBuster将自动根据知识数据库进行检查，并报告它们是否真实。对于必须将人类带入循环的新颖索赔，该系统将提供算法和计算工具，以帮助外行和专业人员理解和审查索赔。ClaimBuster在完成拟议的工作后，将成为有史以来第一个用于广泛事实索赔的自动事实核查系统。它的使用将扩大到验证各种类型的叙述，话语和文件，如体育新闻，法律的文件和财务报告中的声明。它可以使大量的潜在用户受益，包括消费者、出版商、企业竞争对手和法律的专业人士等。它通过提高信息的准确性和透明度直接使消费者受益。它可以帮助新闻机构加快事实核查过程，并确保自己的新闻报道的准确性。企业可以使用ClaimBuster来识别竞争对手和自己的报告和新闻稿中的虚假信息。它还帮助律师等专业人士核实文件。ClaimBuster将使用数据库查询，数据挖掘和自然语言处理技术来帮助事实核查。本课题的具体研究任务如下。(1)研究如何对事实主张进行建模并产生其内部表示。为此，该团队将在不同领域中创建索赔模板的分类，基于分类对索赔进行分类，并通过对索赔文本形式的语义解析生成内部表示。这种特定领域的建模和索赔的内部表示将为系统的其他组件提供新颖的方法和系统的，连贯的解决方案。(2)对于算法事实核查，他们将设计新的方法，将索赔转换为结构化查询，关键字查询和自然语言问题。这些对通用和特定领域数据库和知识图谱的查询结果将与索赔本身中嵌入的答案进行比较，以验证索赔是否符合要求。(3)通过将索赔视为参数化查询，他们将开发基于扰动分析的方法，以找到索赔的反驳论点，并从数据集中找到“有趣”的小事实。这些结果将有助于索赔克星在确定“樱桃采摘”索赔-索赔是正确的，但误导。

项目成果

QAGView: Interactively Summarizing High-Valued Aggregate Query Answers

QAGView：交互式总结高价值聚合查询答案

• DOI: 10.1145/3183713.3193566
• 发表时间: 2018
• 期刊: Proceedings of the 2018 International Conference on Management of Data
• 作者: Wen, Yuhao;Zhu, Xiaodan;Roy, Sudeepa;Yang, Jun
• 通讯作者: Yang, Jun

Learning to Sample: Counting with Complex Queries

学习采样：使用复杂查询进行计数

• DOI: 10.14778/3368289.3368302
• 发表时间: 2019
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Walenz, Brett;Sintos, Stavros;Roy, Sudeepa;Yang, Jun
• 通讯作者: Yang, Jun

Introduction to the Special Issue on Combating Digital Misinformation and Disinformation

打击数字错误信息和虚假信息特刊简介

• DOI: 10.1145/3321484
• 发表时间: 2019
• 期刊: Journal of Data and Information Quality
• 作者: Hassan, Naeemul;Li, Chengkai;Yang, Jun;Yu, Cong
• 通讯作者: Yu, Cong

Guided Bayesian Optimization to AutoTune Memory-Based Analytics

• DOI: 10.1109/icdew.2019.00-22
• 发表时间: 2019-04
• 期刊: 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)
• 作者: Mayuresh Kunjir

I-Rex: Interactive Relational Query Explainer for SQL

I-Rex：SQL 交互式关系查询解释器

• DOI: 10.14778/3415478.3415528
• 发表时间: 2020
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Miao, Zhengjie;Chen, Tiangang;Bendeck, Alexander;Day, Kevin;Roy, Sudeepa;Yang, Jun
• 通讯作者: Yang, Jun