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Messages for: "Artificial Intelligence for Health Message Generation: An Empirical Study Using a Large Language Model (LLM) and Prompt Engineering"
- Author(s):
- Ralf Schmaelzle (see profile)
- Date:
- 2023
- Group(s):
- MSU Health and Risk Communication Center Message Vault
- Item Type:
- Data set
- Permanent URL:
- https://doi.org/10.17613/pzvk-2n28
- Abstract:
- This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. We used prompt engineering to generate awareness messages about folic acid and compared them to the most retweeted human-generated messages via human evaluation with the university and young adult women samples. We also conducted computational text analysis to examine the similarities between the AI-generated messages and human generated tweets in terms of content and semantic structure. The results showed that AI-generated messages ranked higher in message quality and clarity across both samples. The computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Overall, these results demonstrate the potential of large language models for message generation. Theoretical, practical, and ethical implications are discussed.
- Metadata:
- xml
- Status:
- Published
- Last Updated:
- 7 months ago
- License:
- Attribution
messageengine2.0.docx.docx