Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language-Reference-Cited by-同舟云学术

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Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language

Published:2024-05-11 Issue: Volume:13 Page:1-20
ISSN:
Container-title:Proceedings of the CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Ding Xiaohan¹^ORCID,Carik Buse¹^ORCID,Gunturi Uma Sushmitha²^ORCID,Reyna Valerie³^ORCID,Rho Eugenia Ha Rim¹^ORCID

Affiliation:

1. Computer Science, Virginia Tech, United States and Computer Science, Virginia Tech, United States

2. IBM, United States and IBM, United States

3. Human Neuroscience Institute, Cornell University, United States and Human Neuroscience Institute, Cornell University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613904.3642117

Reference124 articles.

1. Saifuddin Ahmed and Muhammad Ehab Rasul. 2022. Social Media News Use and COVID-19 Misinformation Engagement: Survey Study. Journal of Medical Internet Research 24 (2022). https://api.semanticscholar.org/CorpusID:252109554

2. Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

3. View, Like, Comment, Post: Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations

4. Towards Improving Causality Mining using BERT with Multi-level Feature Networks

5. Hossein Azarpanah, Mohsen Farhadloo, Rustam M. Vahidov, and Louise Pilote. 2021. Vaccine hesitancy: evidence from an adverse events following immunization database, and the role of cognitive biases. BMC Public Health 21 (2021). https://api.semanticscholar.org/CorpusID:237522699

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