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，例如在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。在这些场景中，数据可视化通常用于传达对个人决策至关重要的信息。例如，飓风路径的可视化可以告知受影响的人群是否需要做好准备或疏散；而有关特定地区疾病流行情况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，可视化可能存在缺陷，这可能导致对数据的误解，并且在危机中导致做出具有负面后果的决策。该项目旨在确定可视化的哪些方面使其被广泛共享，识别可视化可能存在的缺陷，并向社交媒体用户发出警告。最终，该项目可以帮助普通民众更好地应对危机，并有助于提高可视化素养。最后，该项目还将培训两名研究生，为本科生研究提供机会，并策划可供教育工作者教授可视化设计时使用的材料。这些目标将通过应用现有的和新颖的方法来实现，例如主题建模并计算与最近危机相关的社交媒体帖子的三个大型数据集的社会关注度。使用定性编码方法，将开发设计问题的分类法。该分类法将用于标记大型数据集。最后，将开发一个插件形式的原型干预，该插件会警告有问题的可视化，但也使用户能够对他们遇到的可视化问题进行分类。该项目过程中编译的数据集和注释将公开共享。创建的软件将在宽松的、非病毒式的开源许可证下发布。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Misleading Beyond Visual Tricks: How People Actually Lie with Charts

超越视觉技巧的误导：人们实际上是如何用图表说谎的

• DOI: 10.1145/3544548.3580910
• 发表时间: 2023-04
• 期刊: Proceedings of the CHI Conference on Human Factors in Computing Systems
• 作者: Lisnic, Maxim;Polychronis, Cole;Lex, Alexander;Kogan, Marina
• 通讯作者: Kogan, Marina