Posted June 17, 2020
Top photo by Lewis Zeigler
The expectation South Carolinians will wear face masks to combat the spread of the coronavirus is a divisive one on social media, according to the University of South Carolina Social Media Insights Lab.
The lab analyzed more than 56,000 posts about COVID-19 made since the beginning of June and found the volume of comments about masks now has surpassed posts about new cases. Also, mask comments are the most opinionated.
“People are concerned about new cases, but they are angry about masks,” said Kaitlyn Park, Insights Lab manager. “We found more anger, fear and disgust associated with masks than with any other subject related to COVID-19.”
Those emotions cannot be easily categorized. Some people are angry about having to wear a mask; others are angry about people not wearing masks.
A deeper analysis of posts specifically about the possibility mask usage might be required in some circumstances shows 52 percent support the idea, 17 percent were opposed and 30 percent did not express an opinion. One part of this conversation involves whether restaurant employees should be required to wear face coverings. In analyzing 174 posts on this question since June 1, the lab found a split of opinion with slightly more users supporting mask usage.
Among those who do not support requiring restaurant employees to mask up, business and personal freedom were major topics of discussion. Others said they only would eat in a restaurant if everyone working there was wearing masks. Some comments came from outside South Carolina from people disappointed with the state’s laissez fare attitude about masks.
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.
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.