Abstract
Design, Dissemination, and Disinformation in Viral Maps
Highlights
Google Cloud Vision analysis of Field’s map highlights a range of election and cartographic entities that it finds relevant to the original posting (Figure 1)
Extrapolating from one map to the millions that appear each year on social media, it becomes plausible to apply machine learning methods to characterize their design and web context, even from streaming sources, as these methods are already built to support real-time analysis of streaming data
Indirect types of engagement can include the number of people who saw an item in their social media feed, and the potential audience who may have the opportunity to see an item in their social media feeds
Summary
Google Cloud Vision analysis of Field’s map highlights a range of election and cartographic entities that it finds relevant to the original posting (Figure 1). We have used Google Cloud Vision to characterize the design and dissemination of a viral map created and shared by Kenneth Field, a cartographer at Esri. In March of 2018, Field tweeted an image of a dot-density map showing the 2016 United States Presidential Election results.
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