Empathic Responding for Digital Interpersonal Emotion Regulation via Content Recommendation
Akriti Verma, Shama Naz Islam, Valeh Moghaddam, Adnan Anwar, Sharon Horwood · International Journal of Human-Computer Interaction · 2024
Interpersonal communication is key in managing people's emotions on digital platforms. Studies have shown that people use social media to regulate their emotions and find support for rest and recovery. However, these platforms are not designed for emotion regulation (ER), which limits their effectiveness in this regard. To address this, we propose to enhance interpersonal emotion regulation (IER) on online platforms through content recommendation. The objective is to empower users to regulate their emotions while actively or passively engaging in online platforms by crafting media content that aligns with IER strategies, particularly empathic responding. The proposed system aims to facilitate both user-initiated and system-initiated emotion regulation for real-time IER practices. Our mixed-method research includes analyzing 37.5K Reddit posts and a user survey to develop a Contextual Multi-Armed Bandits (CMAB) recommendation system. The experimentation shows that the empathic recommendations generated by the proposed recommendation system are preferred by users over widely accepted ER strategies such as distraction and avoidance.