Posted Aug. 6, 2020
Photo by Van Kornegay
South Carolina’s passion for college football is reflected on social media. Thousands of people are looking forward to a fall season but questioning what it will look like during a global pandemic.
The Social Media Insights Lab at the University of South Carolina analyzed more than 68,000 football-related posts made in the state since July 1. The dominant themes were adjustments to the season, how college football will look this year, the importance of wearing masks to bring the coronavirus under control and how COVID-19 could affect popular rivalry games.
“We used artificial intelligence to better understand how South Carolinians feel about the possible return of football,” said Kaitlyn Park, Insights Lab manager. “We found the usual buzz and excitement about the upcoming season has been replaced with restraint. People are sad that the one thing the South counts on, college football, will be different this year.”
The importance of wearing masks was a common theme and one of the top influencers in these conversations was Gov. Henry McMaster, who tweeted, “Wear a mask and social distance now so we can enjoy high school and college football in South Carolina this fall.”
Many on social media are concerned about the fact there will be no Carolina-Clemson game this year, a rivalry that has been played every year since 1910. “Many certainly expressed disappointment over the loss of the game,” said Park. “However there were even greater concerns about whether football would or should be played this year.”
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.
For media inquiries or to request graphic files, contact Rebekah Friedman at email@example.com or 803-576-7270.
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.