| Autor: |
WHITE, Brianna, KUMSA, Fekede, SINGH, Nupur, MELTON, Chad, SHABAN-NEJAD, Arash |
| Zdroj: |
Studies in Health Technology & Informatics; 2023, Vol. 309, p304-305, 2p |
| Abstrakt: |
As social media use has grown in recent years, ease of access and rapid data collection through online social media has permitted researchers to measure and track sentiments related to emerging public health threats. Herein, we explore the possibilities of examining messaging shared via social media networks for sentiment classification as it relates to women's reproductive healthcare, especially access to abortion. In our previous works, our team has successfully employed various natural language processing (NLP) models for the analysis of social media shared sentiments. This study reports a work-in-progress on the similar use of finetuned NLPs (i.e., DistilRoBERTa) to collect/analyze the sentiments of sociobehavioral data shared via social networks to uncover a correlation between reproductive-related misinformation (i.e., access to abortion) and public sentiments/discourse direction. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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