The Misleading Analysis Framework and Misleading Methods of Data Visualization in Data Journalism
[Research purpose]In recent years,there has been a surge in misleading information in data journalism visualization,leading to challenges in news and information security and governance.Analyzing the framework of misleading information in data journalism vi-sualization and identifying misleading methods can provide valuable insights for fact-checking,prevention of misinformation,and enhan-cing digital literacy.[Research method]This study involved a review and synthesis of relevant literature on visual misinformation and a-nalysis recent erroneous cases.A theoretical analysis framework was constructed,and specific misleading methods were categorized based on this framework.[Research conclusion]Drawing from visual analysis frameworks and the data journalism production-consumption process,we developed a pipeline for analyzing misleading information in data journalism visualization,consisting of five stages:data col-lection analysis,visualization design,narrative editing,paper layout,and reader consumption.Subsequently,11 sub-stages were identi-fied within the design and practice process,encompassing 73 detailed misleading methods.Finally,the study provides insights into future research from the perspectives of understanding and correcting misinformation,deepening disciplinary specialization,and cognitive educa-tion.
data journalismdata visualizationinformation securitydata misleadingmisinformationmisleading methoddigital litera-cy
万方数据
维普
