A Social Media Data Analysis of General and Educational Use of ChatGPT: Understanding Emotional Educators
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Generative Artificial Intelligence (GAI) tools, such as ChatGPT, rapidly revolutionized the world in many contexts, including education. This study reports on early adopters’ perspectives, attitudes, sentiments, and discourses regarding the general and educational use of ChatGPT using X (former Twitter) data. Text mining, sentiment analysis, and topic modeling techniques were used in the analysis. The results revealed the vast applicability of GAI tools and the versatility of ChatGPT to be used in different domains and by users from diverse backgrounds and of different expertise. The use of GAI, specifically ChatGPT, was highlighted in the areas of education, enterprises, cybersecurity, marketing and content creation, entertainment, virtual assistants, chatbots, and investment. Positive sentiments significantly outnumbered the negative ones and the public mostly expressed trust, anticipation, and joy when referring to both the general and educational use of ChatGPT. A statistically significant difference was observed with educational dataset containing considerably fewer neutral tweets. Therefore, it can be inferred that educators were more expressive and emotional when discussing the adoption and use of new technologies in their classrooms and in educational settings. Moreover, the study findings have two key implications. First, social media, such as Twitter, may be useful to discuss and analyze new innovations, such as ChatGPT. Second, this study reveals a disparity between the general public and education sector in their perspectives on innovations, with the educators exhibiting more extreme emotions. This finding raises concerns about the digital divide and the potential of educators acting impulsively based on emotions.
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In the education sector there is serious concern about academic integrity and plagiarism regarding GenAI.
“The public discourse surrounding GenAI has been mostly positive, but in the education sector there is serious concern about academic integrity and plagiarism (Dehouche, 2021;Lampropoulos et al., 2023;Sullivan et al., 2023;Yeo, 2023).”
From Not quite eye to A.I.: student and teacher perspectives on the use of generative artificial intelligence in the writing process (2023) · cites this paper as Lampropoulos et al., 2023
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AI-generated feedback on writing: insights into efficacy and ENL student preference
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- Authors
- Lampropoulos, G., Ferdig, R. E., & Kaplan-Rakowski, R.
- Affiliations
- Kent State University (US)
- ORCID
- Ferdig, Richard E.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4379351668
- Cited by
- 33
- References
- 41
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 16 · 2023: 4
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
- Keywords
- ChatGPT, generative AI, teacher attitudes, early adopters, Twitter discourse, AI in teaching, digital divide, trust in AI
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