Machine and Human Understanding of Empathy in Online Peer Support: A Cognitive Behavioral Approach

要旨

Online peer support provides space for individuals to connect with others and seek support. However, while empathy is critical for effective support, studies have found that highly empathetic support on these platforms can be rare. Using data from online peer support platforms, we conducted a mixed-methods analysis to study the factors that lead to support seekers’ perceived empathy. We found that CBT techniques like active listening and reflective restatements, along with fostering a space for exploration, increase perceived empathy, whereas rigid adherence to structure, misalignment of concerns, and lack of emotional validation can contribute to low perceived empathy. In addition, despite the high levels of empathy reported by most support seekers (85%), computational models reported low averaged empathy (1.69/6). Lastly, we propose that empathy is not a quantifiable metric and that future algorithmic empathy measurements require human perspectives.

著者
Sara Syed
Brown University, Providence, Rhode Island, United States
Zainab Iftikhar
Brown University, Providence, Rhode Island, United States
Amy Wei. Xiao
Brown University , Providence, Rhode Island, United States
Jeff Huang
Brown University, Providence, Rhode Island, United States
論文URL

https://doi.org/10.1145/3613904.3642034

動画

会議: CHI 2024

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)

セッション: Social Support for Wellbeing

316A
5 件の発表
2024-05-13 23:00:00
2024-05-14 00:20:00