EAGER: Understanding and Mitigating Misinformation in Visualizations on Social Media

EAGER：理解和减少社交媒体可视化中的错误信息

• 批准号: 2041136
• 负责人: Alexander Lex
• 金额: $ 20万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041136&HistoricalAwards=false
• 关键词: EAGER; Understanding; Mitigating; Misinformation; Visualizations

In a time of crisis, such as during a hurricane or a global pandemic, social media is an important source of information for the general population. In these scenarios, data visualizations are often used to convey information that is critical for decision making by individuals. For example, a visualization of the path of a hurricane can inform the affected population about the need to prepare or evacuate; while a visualization about the prevalence of a disease in a certain area can inform personal choices, such as limiting interactions with others during a relevant time period. Visualizations, however, can be flawed, which can lead to misinterpretation of the data, and, in a crisis, lead to decisions with negative consequences. This project seeks to identify aspects of visualizations that makes them widely shared, identify flaws a visualization might have, and warn social media users about them. Ultimately, this project can lead to better responses to a crisis by the general population, and contribute to improving visualization literacy. Finally, this project will also enable the training of two graduate students, provide opportunities for undergraduate research, and curate material that can be leveraged by educators teaching about visualization design.These goals will be achieved by applying existing and novel methods, such as topic modeling and calculating measures of social attention, to three large dataset of social media posts related to recent crisis. Using a qualitative coding approach, a taxonomy of design problems will be developed. This taxonomy will be used to label a large dataset. Finally, a prototype intervention in the form of a plug-in that warns of problematic visualizations, but also enables users to classify problems with visualizations they encounter, will be developed. The dataset and the annotations compiled in the course of this project will be shared publicly. The software created will be released under a permissive, non-viral open source license.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.

在危机时期，例如飓风或全球大流行期间，社交媒体是普通民众的重要信息来源。在这些情况下，数据可视化通常用于传达对于个人决策至关重要的信息。例如，飓风道路的可视化可以告知受影响的人群准备或撤离的需求；尽管关于某个领域疾病患病率的可视化可以为个人选择提供信息，例如在相关时间段内限制与他人的互动。然而，可视化可能会存在缺陷，这可能导致对数据的误解，并在危机中导致决策带来负面后果。该项目旨在确定可视化的各个方面，这些方面使它们广泛共享，识别可能存在的缺陷，并警告社交媒体用户。最终，该项目可能会导致一般人群对危机的更好回应，并有助于提高可视化素养。最后，该项目还将能够培训两名研究生，为本科研究提供机会，并通过教授有关可视化设计的教育工作来利用这些材料。这些目标将通过应用现有和新颖的方法（例如，将主题建模和计算社会关注度量衡量标准）实现，以与最近与最近的Crisis相关的大型社会媒体数据集来实现。使用定性编码方法，将开发出设计问题的分类法。该分类法将用于标记大型数据集。最后，将以插件的形式进行原型干预，该插件警告有问题的可视化，但也使用户能够将遇到的可视化问题分类。该项目过程中编制的数据集和注释将公开共享。创建的软件将根据允许的非病毒开源许可发布。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。

项目成果

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

• DOI: 10.1145/3544548.3580910
• 发表时间: 2023-01-01
• 期刊: PROCEEDINGS OF THE 2023 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, CHI 2023
• 作者: Lisnic，Maxim;Polychronis，Cole;Kogan，Marina
• 通讯作者: Kogan，Marina