Posted June 30, 2020
Top image used by permission: Josh Bell at the Sun News
As the July Fourth weekend approaches and South Carolina authorities are urging people to stay at home to protect against the spread of the coronavirus, Myrtle Beach is the subject of negative social media conversations across the country.
An analysis by the University of South Carolina Social Media Insights Lab of 5,494 posts since June 22 shows there are three times as many negative comments about the beach resort than positive ones. The heaviest concentration of posts came from South and North Carolina, California, Ohio and Kentucky.
The negative sentiment has been driven by reports of people contracting COVID-19 during visits to Myrtle Beach.
- In West Virginia, 20 coronavirus cases have been linked to visiting the Grand Strand.
- In Ohio, 17 high school students tested positive after a trip to Myrtle Beach.
- In Virginia, about 100 cases among young people have been linked to a beach week there.
“Myrtle Beach has been slower than other communities to enact rules about wearing masks and social distancing,” said Kaitlyn Park, Insights lab manager. “On social media, some people are questioning if the community has taken appropriate measures and they wonder why tourists are going there?”
In looking specifically at posts in South Carolina, some people are criticizing tourists for their behavior, saying that they are making their holiday more important than protecting themselves from the coronavirus.
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.