What Are the Public’s Concerns About ChatGPT? A Novel Self-Supervised Neural Topic Model Tells You

Rui Wang, Xing Liu, Peng Ren, Shuyu Chang, Zhengxin Huang (), Haiping Huang and Guozi Sun
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Rui Wang: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Xing Liu: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Peng Ren: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Shuyu Chang: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Zhengxin Huang: Department of Computer Science, Youjiang Medical University for Nationalities, Baise 533000, China
Haiping Huang: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Guozi Sun: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Mathematics, 2025, vol. 13, issue 2, 1-22

Abstract: The recently released ChatGPT, an artificial intelligence conversational agent, has garnered significant attention in academia and real life. A multitude of early ChatGPT users have eagerly explored its capabilities and shared their opinions on social media, providing valuable feedback. Both user queries and social media posts have been instrumental in expressing public concerns regarding this advanced dialogue system. To comprehensively understand these public concerns, a novel Self-Supervised Neural Topic Model (SSTM), which formulates topic modeling as a representation learning procedure, is proposed in this paper. The proposed SSTM utilizes Dirichlet prior matching and three regularization terms for improved modeling performance. Extensive experiments on three publicly available text corpora (Twitter Posts, Subreddit and queries from ChatGPT users) demonstrate the effectiveness of the proposed approach in extracting higher-quality public concerns. Moreover, the SSTM performs competitively across all three datasets regarding topic diversity and coherence metrics. Based on the extracted topics, we could gain valuable insights into the public’s concerns regarding technologies like ChatGPT, enabling us to formulate effective strategies to address these issues.

Keywords: text mining; information extraction; neural topic model; ChatGPT; social media analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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