“What Happens, What Helps, What Hurts”: A Qualitative Analysis of User Experiences With Large Language Models for Mental Health Support

Yimeng Zhang, Xu Li, Junfeng Zhu, Zhongzhen Sheng, Tony Rousmaniere

Practice Innovations 2026

This study qualitatively examines users' experiences with large language models for mental health support, focusing on what users perceive as helpful, harmful, and consequential in their interactions with these systems.

[Paper]

Wenchuan Earthquake Enhanced Collectivistic Expression in People's Daily

Penghao Mao, Junfeng Zhu, X. Ren

Proceedings of the 11th International Conference on Behavioural and Social Computing (BESC), IEEE 2024

This study tests whether language-based psychological signals reflect a general response to real-world crises by extending the analysis to the Wenchuan earthquake. The results show that large-scale textual data can capture shifts in collective psychological states following acute societal trauma.

Machine Learning Approach to Sentiment Recognition from Periodic Reports

Junfeng Zhu, X. Ren

Proceedings of the 3rd International Conference on Computer Communication and Artificial Intelligence (CCAI), IEEE 2023

abstract: >- Using data from 4,142 publicly offered mutual funds in China, this study constructs a fund-manager sentiment index from periodic reports using natural language processing. The results show that managers’ positive and negative market sentiment signals are closely associated with stock-market movements, demonstrating the potential of NLP to measure psychological variables at scale.