The Suasory Force of Sticky Messages An Application to the Application of Sunscreen

Stickiness refers to the set of persuasive message properties: simple, unexpected, concrete, credible, emotional, and stories (SUCCES). Heath and Heath (2007) argue that a sticky message is expected to be more memorable and hence more persuasive than a non-sticky message. A 2 (sticky v. non-sticky message) x 3 (pretest v. posttest v. delayed test) longitudinal experiment is employed to examine the persuasiveness of sticky messages on applying sunscreen. Results of a mixed model analysis of variance show that the sticky message produces attitudes and behaviors more favorable to the message recom- mendation than the non-sticky message. Specifically, a time × message induction non- additive effect was found, which sustained only in the sticky message condition across time. Despite this interesting effect, its explanation remains elusive

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Messages for: "Artificial Intelligence for Health Message Generation: An Empirical Study Using a Large Language Model (LLM) and Prompt Engineering"

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

Harnessing AI for Health Message Generation: The Folic Acid Message Engine

ABSTRACT Background: Communication campaigns utilizing social media can raise public awareness, but they are difficult to sustain. One barrier is the need to constantly generate and post novel, yet on-topic messages, which creates a resource-intensive bottleneck. Objective: Here, we harness the latest advances in artificial intelligence (AI) to build a system that can generate a large number of candidate messages, which could be used for a campaign. The topic of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example, but the system can work with other topics as well. Methods: We used the Generative-Pre-trained-Transformer-2 (GPT2) architecture, a machine learning model trained on a large natural language corpus, and fine tuned it using a dataset of auto-downloaded tweets about #folicacid. The fine tuned model was then used as a message engine, that is to create new messages about this topic. We carried out an online study to gauge how human raters evaluate the AI-generated tweet messages compared to original, human-crafted messages. Results: We find that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Online raters evaluated the clarity and quality of a selected sample AI-generated tweets as on par with human-generated ones. Overall, these results show that it is feasible to use such a message engine to suggest messages for online campaigns. Conclusions: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage

Messages for: "Artificial Intelligence for Health Message Generation: An Empirical Study Using a Large Language Model (LLM) and Prompt Engineering"

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

Bewertungssystem fuer die Generalueberholung von Montageanlagen Ein Beitrag zur wirtschaftlichen Gestaltung geschlossener Facility-Management-Systeme im Anlagenbau

SIGLEAvailable from TIB Hannover: RO 3280(104) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman