This dissertation investigates how to make text-based computer-delivered health messaging more effective through the use of verbal cues.
The first study focuses on generating diverse and efficacious health messages. It develops a system called "Directed Diversity" that uses word embeddings to automatically select diverse example prompts to show to crowd workers, who then write messages based on those prompts. Through a series of user studies, the researchers found that Directed Diversity led to more diverse and informative messages compared to baseline conditions.
The second study examines the effect of a health chatbot's conversational style on user perceptions and the quality of information shared. Two user studies on Amazon Mechanical Turk found that users perceived a chatbot using a formal conversational style as more competent and appropriate, especially when discussing sensitive medical information. Users also provided more specific and actionable responses when conversing with a formally-styled chatbot.
The final study investigates how a chatbot should reference a user's previous utterances. A three-week longitudinal study found that while users found chatbots that referenced previous utterances (either verbatim or paraphrased) more intelligent and engaging, the verbatim referencing also raised privacy concerns. Interviews provided further insights on user preferences and concerns.
Overall, this work demonstrates that generating diverse health messages, adopting an appropriate formal conversational style, and referencing previous user utterances in a paraphrased manner can improve the effectiveness of text-based computer-delivered health messaging.
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by Samuel Rhys ... pada arxiv.org 04-23-2024
https://arxiv.org/pdf/2404.13633.pdfPertanyaan yang Lebih Dalam