Beyond the Words: Analysis and Detection of Self-Disclosure Behavior during Robot Positive Psychology Interaction

Abstract

Self-disclosure is an important part of mental health treatment process. As interactive technologies are becoming more widely available, many AI agents for mental health prompt their users to self-disclose as part of the intervention activities. However, most existing works focus on linguistic features to classify self-disclosure behavior, and do not utilize other multi-modal behavioral cues. We present analyses of people's non-verbal cues (vocal acoustic features, head orientation and body gestures/movements) exhibited during self-disclosure tasks based on the human-robot interaction data collected in our previous work. Results from the classification experiments suggest that prosody, head pose, and body postures can be independently used to detect self-disclosure behavior with high accuracy (up to 81%). Moreover, positive emotions, high engagement, self-soothing and positive attitudes behavioral cues were found to be positively correlated to self-disclosure. Insights from our work can help build a self-disclosure detection model that can be used in real time during multi-modal interactions between humans and AI agents.