{"title":"美国的大规模枪击事件在增加吗?了解对政治事件的不同定义如何影响人们对可视化预期趋势的看法。","authors":"Poorna Talkad Sukumar, Maurizio Porfiri, Oded Nov, Melanie Tory, Daniel Keefe","doi":"10.1109/MCG.2024.3402790","DOIUrl":null,"url":null,"abstract":"<p><p>Visualizations of mass shooting incidents in the United States appearing in the media can influence people's beliefs and attitudes. However, different data sources each use their own definition of mass shootings, resulting in varying counts and trends of these incidents across the sources. To investigate the effects of these varying definitions on public perceptions, we conducted a crowdsourced study using data from four sources-Mother Jones, Mass Shooter Database, Everytown for Gun Safety, and The Washington Post. We used one or more line plots, with or without explicitly providing the definition, to see how these variations affect viewers' understanding of a 10-year trend in mass shooting frequency. We found that, depending on the data shown, participants' perceptions of the trend changed in both directions (i.e., more or less increasing) compared to their prestudy perceptions. We discuss how data from a single source can influence people's perceptions, and how visualizing data from multiple sources (e.g., superimposed line graphs) can enable more transparent communication. Our work has implications for other media and public visualizations, highlighting the importance of embracing pluralistic approaches to enquiry, especially when dealing with data of significant importance and consequence.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"44 4","pages":"140-149"},"PeriodicalIF":1.7000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Are Mass Shootings in the U.S. Increasing? Understanding How Differing Definitions of Politically Charged Events Impact People's Perceptions of Expected Trends in Visualizations.\",\"authors\":\"Poorna Talkad Sukumar, Maurizio Porfiri, Oded Nov, Melanie Tory, Daniel Keefe\",\"doi\":\"10.1109/MCG.2024.3402790\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Visualizations of mass shooting incidents in the United States appearing in the media can influence people's beliefs and attitudes. However, different data sources each use their own definition of mass shootings, resulting in varying counts and trends of these incidents across the sources. To investigate the effects of these varying definitions on public perceptions, we conducted a crowdsourced study using data from four sources-Mother Jones, Mass Shooter Database, Everytown for Gun Safety, and The Washington Post. We used one or more line plots, with or without explicitly providing the definition, to see how these variations affect viewers' understanding of a 10-year trend in mass shooting frequency. We found that, depending on the data shown, participants' perceptions of the trend changed in both directions (i.e., more or less increasing) compared to their prestudy perceptions. We discuss how data from a single source can influence people's perceptions, and how visualizing data from multiple sources (e.g., superimposed line graphs) can enable more transparent communication. Our work has implications for other media and public visualizations, highlighting the importance of embracing pluralistic approaches to enquiry, especially when dealing with data of significant importance and consequence.</p>\",\"PeriodicalId\":55026,\"journal\":{\"name\":\"IEEE Computer Graphics and Applications\",\"volume\":\"44 4\",\"pages\":\"140-149\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Computer Graphics and Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1109/MCG.2024.3402790\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Graphics and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/MCG.2024.3402790","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
Are Mass Shootings in the U.S. Increasing? Understanding How Differing Definitions of Politically Charged Events Impact People's Perceptions of Expected Trends in Visualizations.
Visualizations of mass shooting incidents in the United States appearing in the media can influence people's beliefs and attitudes. However, different data sources each use their own definition of mass shootings, resulting in varying counts and trends of these incidents across the sources. To investigate the effects of these varying definitions on public perceptions, we conducted a crowdsourced study using data from four sources-Mother Jones, Mass Shooter Database, Everytown for Gun Safety, and The Washington Post. We used one or more line plots, with or without explicitly providing the definition, to see how these variations affect viewers' understanding of a 10-year trend in mass shooting frequency. We found that, depending on the data shown, participants' perceptions of the trend changed in both directions (i.e., more or less increasing) compared to their prestudy perceptions. We discuss how data from a single source can influence people's perceptions, and how visualizing data from multiple sources (e.g., superimposed line graphs) can enable more transparent communication. Our work has implications for other media and public visualizations, highlighting the importance of embracing pluralistic approaches to enquiry, especially when dealing with data of significant importance and consequence.
期刊介绍:
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics, visualization, virtual and augmented reality, and HCI. From specific algorithms to full system implementations, CG&A offers a unique combination of peer-reviewed feature articles and informal departments. Theme issues guest edited by leading researchers in their fields track the latest developments and trends in computer-generated graphical content, while tutorials and surveys provide a broad overview of interesting and timely topics. Regular departments further explore the core areas of graphics as well as extend into topics such as usability, education, history, and opinion. Each issue, the story of our cover focuses on creative applications of the technology by an artist or designer. Published six times a year, CG&A is indispensable reading for people working at the leading edge of computer-generated graphics technology and its applications in everything from business to the arts.