Year: 2024
Type: article
Abstract: This article examines the distortion of data and its visualization in the context of Covid-19 in Italy. While data visualization has become prevalent across various scientific disciplines, it often suffers from being overly intricate, inappropriate for the data type, or capable of causing perceptual biases and data falsification. The surplus of digital data and its subsequent visualization can lead to the manipulation of information, crafting narratives that diverge from official communications and aim to undermine their credibility and accuracy. This article highlights the necessity for properly disseminating data literacy and investigates data visualization's epistemological and methodological dimensions, focusing specifically on the Italian scenario. Misrepresentation of COVID-19 data is characterized by the distortion and misrepresentation of the pandemic data collected, processed, and presented. Through an empirical case study, the article underscores the imperative to develop and utilize data visualization techniques that faithfully and accurately depict data.
Source: Society Register
Authors: Giovannipaolo Ferrari, Paolo Diana
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402891184