"Sticks and stones may break my bones," the old adage goes. "But words will never hurt me."
Tell that to Eugenia Rho, assistant professor in the Department of Computer Science, and she will show you extensive data that prove otherwise.
Her Society + AI & Language Lab has shown that
- Police language is an accurate predictor of violent interactions with Black motorists.
- Broadcast media bias and social media echo chambers have put American democracy at risk.
Now, Rho's research team in the College of Engineering has turned to another question: what effects did social media rhetoric have on COVID-19 infection and death rates across the United States, and what can policymakers and public health officials learn from that?
"A lot of studies just describe what's happening online. Often they do not show a direct link with offline behaviors," Rho said. "But there is a tangible way to connect online behavior with offline decision making."
Cause and effect
During the COVID-19 pandemic, social media became a mass gathering place for opposition to public health guidance, such as mask wearing, social distancing, and vaccines. Escalating misinformation encouraged widespread disregard for preventive measures and led to soaring infection rates, overwhelmed hospitals, health care worker shortages, preventable deaths, and economic losses.
During a one-month period between November and December 2021, more than 692,000 preventable hospitalizations were reported among unvaccinated patients, according to a 2022 study published in the Yale Journal of Biology and Medicine. Those hospitalizations alone cost a staggering $13.8 billion.
In the study, Rho's team, including Ph.D. student Xiaohan Ding, developed a technique that trained the chatbot GPT-4 to analyze posts in several banned subreddit discussion groups that opposed COVID-19 prevention measures. The team focused on Reddit because its data was available, Rho said. Many other social media platforms have barred outside researchers from using their data.
Rho's work is grounded in a social science framework called Fuzzy Trace Theory that was pioneered by Valerie Reyna, a Cornell University professor of psychology and a collaborator on this Virginia Tech project. Reyna has shown that individuals learn and recall information better when it is expressed in a cause and effect relationship, and not just as rote information. This holds true even if the information is inaccurate or the implied connection is weak. Reyna calls this cause-and-effect construction a "gist."
