Posted June 25, 2020
Top photo from Pixabay
The likelihood South Carolina public schools will not return to in-person education in the fall has been a relatively small and neutral topic of social media conversations in the state, with most posts expressing neither positive nor negative sentiment.
The University of South Carolina Social Media Insights Lab analyzed 1,136 posts made since Monday, when S.C. Superintendent of Education Molly Spearman said it would be “extremely difficult” to go back to face-to-face instruction in the fall. The lab found that 70 percent of the posts expressed no emotion. Opinions pro and con were split, with 16 percent positive about the possibility and 15 percent negative.
“Most people on social media seemingly have not made up their minds yet about whether keeping students out of school in the fall is a good idea,” said Kaitlyn Park, lab manager. “While some are concerned about how parents will handle virtual schooling, others question how masks and distancing can be enforced among school children.”
The social media accounts with the most influence on these conversations are Superintendent Spearman, the S.C. Department of Education and U.S. Rep. Joe Cunningham, whose call “to get this virus under control” was widely shared.
The question of school reopening is a small part of a broader conversation about the coronavirus. The lab reviewed more than 32,000 posts this week, with the biggest single topic (6,755 posts) being whether people should wear 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.
For media inquiries or to request graphic files, contact Rebekah Friedman at firstname.lastname@example.org 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.