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<p>The rapid advancement of Generative AI (GenAI) has transformed human-computer interaction, particularly in domains requiring personalized guidance, such as nutritional planning. While GenAIs show promise in generating contextual nutritional recommendations, their integration into social interactions and behavioral patterns (which are key aspects of the Internet of Behavior - IoB) remains inadequately understood. This study investigates how social media discussions can inform the development of more effective human-centered GenAI systems for dietary support. Through a comprehensive analysis of 10,219 user-generated contents across Reddit, YouTube, and Bluesky social media platforms, we examine social interaction patterns, behavioral trends, and user experiences with GenAI-generated nutritional content. Our mixed-method approach combines computational analysis of big social data with qualitative assessment of user feedback, revealing crucial insights on the benefits and pitfalls of GenAIs, ranging from their contribution to users’ health and wellness to their inconsistent outputs with human experts. Based on these insights, we recommend three conceptual frameworks that suggest the integration of GenAIs on social media with human experts’ involvement and social media conversational engagements. Furthermore, our user study experiment involving one of the conceptual frameworks, FR3, revealed significant positive effects in users’ attitude, dietary behavior intentions, and outcomes expectancy (<i>p</i>&lt;.001), alongside moderate trust in GenAIs. These findings contribute to the field through: (1) a structured framework for enhancing human-AI collaboration in nutritional guidance, (2) a validated machine learning model for sentiment classification in dietary AI discourse, and (3) empirical, qualitative insights, and evidence-based recommendations for integrating GenAIs for dietary guidance in social spaces. This research advances our understanding for developing socially responsive and human-centered GenAI systems that effectively promote sustainable healthy eating behaviors.</p>

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Towards Social Good: How Can Social Media Inform the Next GenAI’s Collaboration With Humans for Dietary Support?

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Grace Ataguba,
  • Oladapo Oyebode,
  • Alaa Alslaity,
  • George Frempong,
  • Masud Imran,
  • Rita Orji

摘要

The rapid advancement of Generative AI (GenAI) has transformed human-computer interaction, particularly in domains requiring personalized guidance, such as nutritional planning. While GenAIs show promise in generating contextual nutritional recommendations, their integration into social interactions and behavioral patterns (which are key aspects of the Internet of Behavior - IoB) remains inadequately understood. This study investigates how social media discussions can inform the development of more effective human-centered GenAI systems for dietary support. Through a comprehensive analysis of 10,219 user-generated contents across Reddit, YouTube, and Bluesky social media platforms, we examine social interaction patterns, behavioral trends, and user experiences with GenAI-generated nutritional content. Our mixed-method approach combines computational analysis of big social data with qualitative assessment of user feedback, revealing crucial insights on the benefits and pitfalls of GenAIs, ranging from their contribution to users’ health and wellness to their inconsistent outputs with human experts. Based on these insights, we recommend three conceptual frameworks that suggest the integration of GenAIs on social media with human experts’ involvement and social media conversational engagements. Furthermore, our user study experiment involving one of the conceptual frameworks, FR3, revealed significant positive effects in users’ attitude, dietary behavior intentions, and outcomes expectancy (p<.001), alongside moderate trust in GenAIs. These findings contribute to the field through: (1) a structured framework for enhancing human-AI collaboration in nutritional guidance, (2) a validated machine learning model for sentiment classification in dietary AI discourse, and (3) empirical, qualitative insights, and evidence-based recommendations for integrating GenAIs for dietary guidance in social spaces. This research advances our understanding for developing socially responsive and human-centered GenAI systems that effectively promote sustainable healthy eating behaviors.