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

意识到网络上的错误信息正成为一个越来越重要的问题，尤其是当信息以新闻故事的形式呈现时，因为(a)人们可能会过度信任看起来像新闻的内容，而无法对其进行批判性评估，(b)这样的故事很容易传播，放大了错误信息的影响。项目团队将使用机器学习方法分析一个大型数据库，该数据库包含被标记为或多或少可能包含错误信息的文章，以及来自通信、心理学和信息科学领域的理论分析，首先描述可能包含错误信息的故事与其他故事的区别。这些特征将被用于构建一个工具，该工具可以调用已知与错误信息相关的给定文章的特征；它们还将用于开发培训材料，以帮助人们做出这些判断。工具和培训材料将通过一系列实验进行测试，在这些实验中，工具和人员在接受培训之前和之后对文章进行评估。其目标是通过提高读者和版主减少错误信息活动影响的能力，对在线话语产生积极影响。该团队将公开模型、工具和培训材料，供其他人在研究、课堂和在线中使用。该团队将使用两种主要方法来描述更有可能包含错误信息的文章。第一种是基于对围绕信息传播和评价的研究写作进行深入分析的社会科学概念解释方法。第二种是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训练，包括经过验证的错误信息示例。两种方法都会考虑内容的特点；它的视觉表现；创造、消费和分享财富的人；以及它所经过的网络。这些模型将被转化为一组加权规则，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。这些利用了一阶逻辑和概率图形模型的优势，允许各种有效的推理方法，并已应用于许多相关问题；这些模型将使用标准的机器学习技术根据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的现有工作以及前两项任务中建模工作得出的启发式指导方针开发培训材料，通过前面描述的实验对其进行评估，并将其与开发的模型一起在线传播。

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.