The authors propose AnnoSense, a set of 15 expert-reviewed guidelines for everyday emotion data collection, derived from survey, interview, and focus group insights from 119 stakeholders.
Critiquing Self-report Practices for Human Mental and Wellbeing Computing at Ubicomp
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abstract
Computing human mental and wellbeing is crucial to various domains, including health, education, and entertainment. However, the reliance on self-reporting in traditional research to establish ground truth often leads to methodological inconsistencies and susceptibility to response biases, thus hindering the effectiveness of modelling. This paper presents the first systematic methodological review of self-reporting practices in Ubicomp within the context of human mental and wellbeing computing. Drawing from existing survey research, we establish guidelines for self-reporting in human wellbeing studies and identify shortcomings in current practices at Ubicomp community. Furthermore, we explore the reliability of self-report as a means of ground truth and propose directions for improving ground truth measurement in this field. Ultimately, we emphasize the urgent need for methodological advancements to enhance human mental and wellbeing computing.
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AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI
The authors propose AnnoSense, a set of 15 expert-reviewed guidelines for everyday emotion data collection, derived from survey, interview, and focus group insights from 119 stakeholders.