人与错误信息的相互作用：理解计算打击错误信息所需的跨学科方法
Human-Misinformation interaction: Understanding the interdisciplinary approach needed to computationally combat false information
摘要：
新技术和社交媒体的普及扩大了错误信息对我们社会的影响。因此，有必要创建计算工具来有效地减轻其影响。这项研究的目的是提供一个关键的概述有关打击错误信息的计算方法。为了这个目的，我提供了一个概述的学术定义的错误信息。我采用了一个框架来研究错误信息，建议关注来源，内容和消费者作为错误信息过程中涉及的三个主要元素，我提供了一个概述，从心理学，媒体研究和认知科学的学科，处理这些元素的文献。使用该框架，我概述了现有的计算方法，这些方法处理1）使用内容进行错误信息检测和事实检查2）识别不可信的来源和社交机器人，以及3）面向消费者的工具和方法，旨在使人类对错误信息具有弹性。我发现，计算机科学和信息技术领域的绝大多数作品都与检测和核实错误信息的内容和来源这一关键任务有关。此外，我发现，计算研究集中在人机交互（HCI）和相关领域的错误信息的消费者是非常稀疏，往往不处理这个过程的微妙之处。大多数现有的接口和系统不太关心工具的可用性，而更关心检测方法的鲁棒性和准确性。利用这项调查，我呼吁对人与错误信息的互动采取跨学科的方法，重点是建立强有力地处理这种复杂的心理/社会现象的方法和工具。
英文摘要：
The prevalence of new technologies and social media has amplified the effects of misinformation on our societies. Thus, it is necessary to create computational tools to mitigate their effects effectively. This study aims to provide a critical overview of computational approaches concerned with combating misinformation. To this aim, I offer an overview of scholarly definitions of misinformation. I adopt a framework for studying misinformation that suggests paying attention to the source, content, and consumers as the three main elements involved in the process of misinformation and I provide an overview of literature from disciplines of psychology, media studies, and cognitive sciences that deal with each of these elements. Using the framework, I overview the existing computational methods that deal with 1) misinformation detection and fact-checking using Content 2) Identifying untrustworthy Sources and social bots, and 3) Consumer-facing tools and methods aiming to make humans resilient to misinformation. I find that the vast majority of works in computer science and information technology is concerned with the crucial tasks of detection and verification of content and sources of misinformation. Moreover, I find that computational research focusing on Consumers of Misinformation in Human-Computer Interaction (HCI) and related fields are very sparse and often do not deal with the subtleties of this process. The majority of existing interfaces and systems are less concerned with the usability of the tools rather than the robustness and accuracy of the detection methods. Using this survey, I call for an interdisciplinary approach towards human-misinformation interaction that focuses on building methods and tools that robustly deal with such complex psychological/social phenomena.
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