The digital whisperer: deciphering topics and sentiments towards ChatGPT on X (Twitter)

Tingyu Zhang & Lei Lei

Online Information Review2026https://doi.org/10.1108/oir-05-2024-0338article
AJG 1ABDC B
Weight
0.50

What the paper says

Purpose ChatGPT, a chatbot developed by OpenAI, has aroused extensive discussion since it was launched in November 2022. The present study aims to explore what is talked about and how the discussion has developed concerning ChatGPT on a social media platform, X (Twitter). Design/methodology/approach We conducted topic modelling and sentiment analysis on a dataset of 446,285 tweets across the first four months after ChatGPT was launched. Findings Results showed that discussions about ChatGPT focused on basic functions and its use in technology, business, education and creative work. Users also expressed concerns regarding cybersecurity, information accuracy and ethics. In addition, the discussion intensity concerning ChatGPT showed a rising trend that corresponded with important updates released by technology corporations. However, there were also periods when interests waned due to public attention distraction. Last, the proportions of positive and negative sentiment decreased, probably because of the contagious spread of neutral sentiment. Originality/value The findings offer valuable contributions to our understanding of the discourse regarding ChatGPT, the dynamics of public attention, and the emotional contagion mechanism on social media.

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https://doi.org/https://doi.org/10.1108/oir-05-2024-0338

Or copy a formatted citation

@article{tingyu2026,
  title        = {{The digital whisperer: deciphering topics and sentiments towards ChatGPT on X (Twitter)}},
  author       = {Tingyu Zhang & Lei Lei},
  journal      = {Online Information Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/oir-05-2024-0338},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.