Posted June 29, 2020
Top photo from Gov. Henry McMaster's Twitter account
South Carolina Gov. Henry McMaster’s statement that a statewide rule requiring face masks to limit the spread of the coronavirus would be unenforceable is drawing criticism on social media.
An analysis by the University of South Carolina Social Media Insights Lab of 1,939 posts since the governor’s comment last Friday found that 34 percent were negative, 16 percent were positive and 50 percent were neutral and contained no emotion.
“We used artificial intelligence to better understand the feelings associated with these comments,” said Kaitlyn Park Insights lab manager. “The most frequent emotions we saw were sadness, disgust and anger.”
In a news conference Friday, the governor said he had no problem with city leaders adopting face mask ordinances, but that it’s “virtually impossible to fashion a statewide rule.” He went on to encourage South Carolinians to wear face masks and practice social distancing. South Carolina has seen a recent surge of COVID-19 cases.
In the social media conversations, the lab found the governor was the top influencer, followed by DHEC and Charleston attorney Mary Louise Ramsdale, who tweeted, “Everyone disgusted by @henrymcmaster’s insipid response to COVID-19…when we needed a leader, he failed us.”
Columbia, Greenville and Charleston all enacted face mask orders last week.
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.