EAGER: Training Computers and Humans to Detect Misinformation by Combining Computational and Theoretical Analysis

EAGER：通过结合计算和理论分析来训练计算机和人类检测错误信息

• 批准号: 1742702
• 负责人: Dongwon Lee
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1742702&HistoricalAwards=false
• 关键词: EAGER; Training; Computers; Humans; Detect

Awareness of misinformation online is becoming an increasingly important issue, especially when information is presented in the format of a news story, because (a) people may over-trust content that looks like news and fail to critically evaluate it, and (b) such stories can be easily spread, amplifying the effect of misinformation. Using machine learning methods to analyze a large database of articles labeled as more or less likely to contain misinformation, along with theoretical analyses from the fields of communication, psychology, and information science, the project team will first characterize what distinguishes stories that are likely to contain misinformation from others. These characteristics will be used to build a tool that calls out characteristics of a given article that are known to correlate with misinformation; they will also be used to develop training materials to help people make these judgments. The tool and training materials will be tested through a series of experiments in which articles are evaluated by the tool and by people both before and after undergoing training. The goal is to have a positive impact on online discourse by improving both readers' and moderators' ability to reduce the impact of misinformation campaigns. The team will make the models, tools, and training materials publicly available for others to use in research, in classes, and online.The team will use two main approaches to characterize articles that are more likely to contain misinformation. The first is a concept explication approach from the social sciences based on a deep analysis of research writing around information dissemination and evaluation. The second is a supervised machine learning approach to be trained on large datasets of labeled articles, including verified examples of misinformation. Both approaches will consider characteristics of the content; of its visual presentation; of the people who create, consume, and share it; and of the networks it moves through. These models will be translated into a set of weighted rules that combine the insights from the two approaches, then instantiated in Markov Logic Networks. These leverage the strengths of both first order logic and probabilistic graphic models, allow for a variety of efficient inference methods, and have been applied to a number of related problems; the models will be evaluated offline against test data using standard machine learning techniques. Finally, the team will develop training materials based on existing work from the International Federation of Library Associations and Institutions and on heuristic guidelines derived from the modeling work in the first two tasks, evaluate them through the experiments described earlier, and disseminate them online along with the developed models.

在线上对错误信息的认识正成为一个越来越重要的问题，尤其是当以新闻故事的形式提供信息时，因为（a）人们可能会过度信任的内容看起来像新闻，并且无法进行严格的评估，并且（b）可以轻松地传播此类故事，扩大误差信息的效果。 使用机器学习方法来分析一个大或多或少地包含错误信息的文章数据库，以及与沟通，心理学和信息科学领域的理论分析，项目团队首先将要首先描述与其他可能包含错误信息的故事的区分。 这些特征将用于构建一种工具，该工具呼吁给定文章的特征，这些特征与错误信息相关。它们还将用于开发培训材料，以帮助人们做出这些判断。 该工具和培训材料将通过一系列实验进行测试，在该实验中，该工具在培训前后都通过工具和人员进行评估。 目的是通过提高读者和主持人减少错误信息运动影响的能力来对在线话语产生积极影响。 该团队将制造模型，工具和培训材料公开可用于其他人在研究，课堂和在线上使用。该团队将使用两种主要方法来表征更可能包含错误信息的文章。 首先是一种基于对信息传播和评估的研究写作的深入分析，一种概念阐明方法。第二个是一种有监督的机器学习方法，可以在标记文章的大型数据集上进行培训，包括经过验证的错误信息示例。 两种方法都将考虑内容的特征。视觉呈现；创造，消费和分享的人中；以及它通过的网络。 这些模型将被翻译成一组加权规则，这些规则结合了两种方法的见解，然后在马尔可夫逻辑网络中实例化。 这些利用一阶逻辑和概率图形模型的优势，允许多种有效的推理方法，并已应用于许多相关问题。将使用标准机器学习技术对测试数据进行离线评估。 最后，该团队将根据国际图书馆协会和机构联合会以及根据前两个任务中的建模工作得出的启发式准则，通过启发式指南开发培训材料，通过前两项任务中的建模工作，通过前面描述的实验对其进行评估，并与开发的模型一起在线传播它们。

项目成果

Authorship Attribution for Neural Text Generation

• DOI: 10.18653/v1/2020.emnlp-main.673
• 发表时间: 2020-11
• 作者: Adaku Uchendu;Thai Le;Kai Shu;Dongwon Lee

MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

• DOI: 10.1109/icdm50108.2020.00037
• 发表时间: 2020-09
• 期刊: 2020 IEEE International Conference on Data Mining (ICDM)
• 作者: Thai Le;Suhang Wang;Dongwon Lee

DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation

• DOI: 10.1145/3394486.3403092
• 发表时间: 2020-01-01
• 期刊: KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
• 作者: Cui, Limeng;Seo, Haeseung;Lee, Dongwon
• 通讯作者: Lee, Dongwon

GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction

• DOI: 10.1145/3394486.3403066
• 发表时间: 2020-01-01
• 期刊: KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
• 作者: Le, Thai;Wang, Suhang;Lee, Dongwon
• 通讯作者: Lee, Dongwon

PROMO for Interpretable Personalized Social Emotion Mining

• DOI: 10.1007/978-3-030-67658-2_15
• 发表时间: 2021-02
• 作者: Jason Zhang;Dongwon Lee