Examining social media as drivers of rhetorical practices
Author: Virginia Tech
Published: 2024/05/12
Post type: Research, Study, Analysis – Peer Reviewed: Yeah
Content: Summary – Definition – Introduction – Major – Related topics
Synopsis: Many studies simply look at Internet activities without linking them to real-world actions. However, there is a clear method for matching online behavior with offline options. This is the first AI research to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of these large language models to identify critical online discussion patterns and pinpoint communication strategies. more effective public health. Simply banning people from online communities, especially in spaces where they are already exchanging and learning health information, can risk delving into conspiracy theories and forcing them onto platforms that don’t moderate content at all. This study can inform how social media companies can work hand-in-hand with public health officials and organizations to engage and better understand what is happening in the public’s minds during public health crises.
Introduction
“Sticks and stones can break my bones,” The old saying goes. “But words will never hurt me.”
Tell Eugenia Rho, associate professor in the Department of Computer Science, and she will show you extensive data that proves otherwise.
Main summary
Its Society + AI & Language Lab has shown that:
- Police language is an accurate predictor of violent interactions with black drivers.
- Media bias and social media echo chambers have put American democracy at risk.
Now, Rho’s research team in the College of Engineering has focused on another question: What effects did social media rhetoric have on COVID-19 infection and death rates in the United States, and what can they learn? policymakers and public health officials?
“Many studies simply describe what happens online. They often don’t show a direct link to offline behaviors,” Rho said. “But there is a tangible way to connect online behavior to offline decision-making.”
Cause and effect
During the COVID-19 pandemic, social media became a mass gathering place for opposition to public health guidelines such as mask-wearing, social distancing, and vaccines. Growing misinformation fostered widespread disregard for preventive measures and led to skyrocketing infection rates, overwhelmed hospitals, shortages of healthcare workers, preventable deaths, and economic losses.
During a one-month period between November and December 2021, more than 692,000 avoidable hospitalizations were reported among unvaccinated patients, according to a 2022 study published in 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 GPT-4 chatbot 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 banned outside researchers from using their data.
Rho’s work is based on a social science framework called Fuzzy Trace Theory that was pioneered by Valerie Reyna, a psychology professor at Cornell University and a collaborator on this Virginia Tech project. Reyna has shown that individuals learn and remember Information is best when expressed in a cause-and-effect relationship, and not simply as rote information. This is true even if the information is inaccurate or the implied connection is weak. Reyna calls this cause and effect construction a “essence.”
The researchers worked to answer four fundamental questions related to what is essential in social networks:
- Do essential patterns significantly predict trends in national health outcomes?
- How can we efficiently predict the substance of social media discourse on a national scale?
- Do essential patterns significantly predict patterns in online participation among users on banned subreddits opposing COVID-19 health practices?
- What kinds of essences characterize how and why people oppose COVID-19 public health practices, and how do these essences evolve over time at key events?
The missing link
Rho’s team used cueing techniques in large language models (LLM), a type of artificial intelligence (AI) program, along with advanced statistics to search for and then track these gist in banned subreddit groups. The model then compared them to COVID-19 milestones such as infection rates, hospitalizations, deaths and related public policy announcements.
The results show that, in fact, social media posts that linked a cause, such as “I received the COVID vaccine” with an effect like “Since then I have felt dead” it quickly appeared in people’s beliefs and affected their offline health decisions. In fact, the total and new daily cases of COVID-19 in the US could be significantly predicted from the volume of essential information in the banned subreddit groups.
This is the first AI research to empirically link social media linguistic patterns to real-world public health trends, highlighting the potential of these large language models to identify critical online discussion patterns and pinpoint communication strategies. more effective public health.
“This study solves a daunting problem: how to connect the cognitive components of meaning that people actually use with the flow of information through social media and the world of health outcomes,” Reyna said. “This cue-based LLM framework that identifies essentials at scale has many potential applications that can promote better health and well-being.”
Big data, big impact
Rho said he hopes this study encourages other researchers to apply these methods to important questions. To that end, the code used in this project will be freely available when the article is published on the Proceedings of the Association for Computing Machinery Conference on Human Factors in Computing Systems. The article also compares the cost of several ways that researchers can analyze large data sets and draw meaningful conclusions at a lower cost. The team will present their findings May 11-16, 2024 in Honolulu, Hawaii.
Outside of academia, Rho said he hopes this work encourages social media platforms and other stakeholders to find alternatives to removing or banning groups that discuss controversial topics.
“Simply banning people from online communities entirely, especially in spaces where they are already exchanging and learning health information, can risk delving deeper into conspiracy theories and forcing them onto platforms that don’t moderate content at all.” said Rho. “I hope this study can inform how social media companies work hand-in-hand with public health officials and organizations to engage and better understand what’s going on in the public’s minds during public health crises.”
Attribution/Source(s):
This peer-reviewed publication titled Tracking the spread of the language epidemic in the United States was chosen for publication by the editors of Disabled World due to its relevance to the disability community. While content may have been edited for style, clarity, or brevity, it was originally written by Virginia Tech and published on 05/12/2024. For more details or clarifications, you can contact Virginia Tech directly on vt.edu Disabled World makes no warranty or endorsement related to this article.
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Cite this page (APA): Virginia Tech. (2024, May 12). Tracking the spread of the language epidemic in the United States. Disabled world. Retrieved May 13, 2024 from www.disabled-world.com/communication/linguistic.php
Permanent link: Tracking the spread of the language epidemic in the United States– Many studies simply look at Internet activities without linking them to real-world actions. However, there is a clear method for matching online behavior with offline options.
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