The recently released artificial intelligence conversational agent, ChatGPT,
has gained significant attention in academia and real life. A multitude of
early ChatGPT users eagerly explore its capabilities and share their opinions
on it via social media. Both user queries and social media posts express public
concerns regarding this advanced dialogue system. To mine public concerns about
ChatGPT, a novel Self-Supervised neural Topic Model (SSTM), which formalizes
topic modeling as a representation learning procedure, is proposed in this
paper. Extensive experiments have been conducted on Twitter posts about ChatGPT
and queries asked by ChatGPT users. And experimental results demonstrate that
the proposed approach could extract higher quality public concerns with
improved interpretability and diversity, surpassing the performance of
state-of-the-art approaches.

通过自监督神经主题模型（SSTM）挖掘公众对 ChatGPT 的关注，实验证明该方法能提取质量更高、可解释性更好、多样性更强的公众关切，超越了现有方法的性能。