^{Posted July 31, 2020}

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Gov. Henry McMaster’s latest steps to reopen South Carolina are being met with skepticism on social media.  An analysis by the University of South Carolina Social Media Insights Lab found more than twice as many people had negative comments about the governor’s plan than supported it.   

On Wednesday, McMaster announced that all businesses including movie theaters and sports venues could reopen Monday under specific guidelines, including:

• Masks must be worn inside
• An occupancy limit of 250 people or 50 percent of capacity
• Restaurants must limit tables to eight people

The Insights Lab used artificial intelligence to review more than 7,000 posts made since Wednesday with more than 56 percent coming from South Carolina. While half the posts were informational and did not contain sentiment, 34 percent were critical of the governor and 16 percent supported him.

“People are worried about their safety, their children and whether the governor and South Carolina as a state have taken the coronavirus seriously enough,” said Insights Lab manager Kaitlyn Park.  “Those posts that support the governor emphasized freedom, the need to reopen schools and help businesses.”

This analysis is consistent with previous Insights Lab reports that showed social media users generally opposed to the governor’s approach to reopening South Carolina. 

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About the Social Media Insights Lab

The lab is part of the College of Information and Communications. It is used for teaching, academic research and public reports intended to help people better understand issues of the day.

The Insights Lab software, Crimson Hexagon, uses artificial intelligence to interpret data. View a full list of reports  and follow the lab on Twitter at @UofSCInsights. 

How is sentiment calculated?
The lab uses software developed by Crimson Hexagon, now known as BrandWatch following a merger. The software gauges the emotional tone of conversations using auto-sentiment artificial intelligence technology. This feature is useful for identifying patterns within large sets of social media data, but it should be noted that auto-sentiment has its limits. For example, it does not always recognize sarcasm, nor does it account for posts which may express more than one emotion.