학술논문

Do You Believe Your (Social Media) Data? A Personal Story on Location Data Biases, Errors, and Plausibility as Well as Their Visualization
Document Type
Periodical
Source
IEEE Transactions on Visualization and Computer Graphics IEEE Trans. Visual. Comput. Graphics Visualization and Computer Graphics, IEEE Transactions on. 28(9):3277-3291 Sep, 2022
Subject
Computing and Processing
Bioengineering
Signal Processing and Analysis
Social networking (online)
Data visualization
Data collection
Data mining
Multimedia Web sites
Image databases
Data integrity
Social media data
Flickr
Panoramio
iNaturalist
data bias
data error
data plausibility
data obfuscation
citizen science
Language
ISSN
1077-2626
1941-0506
2160-9306
Abstract
We present a case study on a journey about a personal data collection of carnivorous plant species habitats, and the resulting scientific exploration of location data biases, data errors, location hiding, and data plausibility. While initially driven by personal interest, our work led to the analysis and development of various means for visualizing threats to insight from geo-tagged social media data. In the course of this endeavor we analyzed local and global geographic distributions and their inaccuracies. We also contribute Motion Plausibility Profiles—a new means for visualizing how believable a specific contributor’s location data is or if it was likely manipulated. We then compared our own repurposed social media dataset with data from a dedicated citizen science project. Compared to biases and errors in the literature on traditional citizen science data, with our visualizations we could also identify some new types or show new aspects for known ones. Moreover, we demonstrate several types of errors and biases for repurposed social media data. Please note that people with color impairments may consider our alternative paper version.