Unveiling the multifaceted public interest in ChatGPT
A study on societal implications and operational realities
DOI:
https://doi.org/10.33621/jdsr.v6i3.33307Keywords:
Artificial Intelligence, ChatGPT, Natural Language Processing, RedditAbstract
The present study was aimed at identifying key topics in online discussions about the use of ChatGPT by examining a large dataset extracted from Reddit social media using natural language processing. A corpus of 159,971 posts about ChatGPT were extracted from a custom-made python-coded Reddit content scraper for posts in the r/ChatGPT subreddit discussions. After cleaning the data, the sample was reduced to 119,853 posts which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the cooccurrence of texts. These clusters were named by a panel of social psychology experts (n=3) by reading typical text segments within each cluster. Four thematic clusters emerged, categorized into two main topics: “Society and AI Integration”, focusing on ethical concerns (32.1%), and “Operational Aspects and Applications”, which delves into technical and practical facets (67.9%). The latter includes clusters like “AI Technical Framework”, “Casual AI Interactions”, and “Human-AI Etiquette”. The Reddit discourse provides a comprehensive understanding of ChatGPT, revealing user priorities like system capabilities and ethical considerations. Notably, the “Human-AI Etiquette” cluster is a new topic less covered in existing literature. The findings underscore the importance of effective prompting for meaningful user engagement with ChatGPT.
References
Alshater, M. (2022). Exploring the role of artificial intelligence in enhancing academic performance: A case study of ChatGPT. Available at SSRN. https://dx.doi.org/10.2139/ssrn.4312358
Alipour, S., Galeazzi, A., Sangiorgio, E., Avalle, M., Bojic, L., Cinelli, M., & Quattrociocchi, W. (2024). Cross-platform social dynamics: an analysis of ChatGPT and COVID-19 vaccine conversations. Scientific Reports, 14(1), 2789. https://doi.org/10.1038/s41598-024-53124-x
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., & Inkpen, K. (2019). Guidelines for human-AI interaction. Proceedings of the 2019 chi conference on human factors in computing systems,
Eberstadt, N. (2022). Men Without Work: Post-Pandemic Edition (2022). Templeton Foundation Press.
Eysenbach, G., & Till, J. E. (2001). Ethical issues in qualitative research on internet communities. Bmj, 323(7321), 1103-1105. https://doi.org/10.1136/bmj.323.7321.1103
Flathmann, C., McNeese, N. J., Schelble, B., Knijnenburg, B., & Freeman, G. (2023). Understanding the impact and design of AI teammate etiquette. Human–Computer Interaction, 1-28. https://doi.org/10.1080/07370024.2023.2189595
Giray, L. (2023). Prompt Engineering with ChatGPT: A Guide for Academic Writers. Annals of Biomedical Engineering, 1-5. https://doi.org/10.1007/s10439-023-03272-4
Goertzel, B. (2014). Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 5(1), 1. https://doi.org/10.2478/jagi-2014-0001
Guerberof-Arenas, A., & Moorkens, J. (2023). Ethics and machine translation: The end user perspective. In Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (pp. 113-133). Springer.
Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A human–machine communication research agenda. New media & society, 22(1), 70-86. https://doi.org/10.1177/1461444819858691
Hois, J., Theofanou-Fuelbier, D., & Junk, A. J. (2019). How to achieve explainability and transparency in human AI interaction. HCI International 2019-Posters: 21st International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II 21,
Jaidka, K., Zhou, A., Lelkes, Y., Egelhofer, J., & Lecheler, S. (2022). Beyond anonymity: Network affordances, under deindividuation, improve social media discussion quality. Journal of Computer-Mediated Communication, 27(1), zmab019. https://doi.org/10.1093/jcmc/zmab019
Jamnik, M. R., & Lane, D. J. (2019). The use of Reddit as an inexpensive source for high-quality data. Practical Assessment, Research, and Evaluation, 22(1), 5. https://doi.org/10.7275/j18t-c009
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2
Keller, T. R., & Klinger, U. (2019). Social bots in election campaigns: Theoretical, empirical, and methodological implications. Political Communication, 36(1), 171-189. https://doi.org/10.1080/10584609.2018.1526238
Koukouvinou, P., & Holmström, J. (2022). AI management beyond the narratives of dystopian nightmares and utopian dreams: A systematic review and synthesis of the literature. European Conference on Information Systems (ECIS), Timisoara, Romania, 18-24 June, 2022.
Lian, Y., Tang, H., Xiang, M., & Dong, X. (2023). Public attitudes and sentiments toward ChatGPT in China: A text mining analysis based on social media. Technology in Society, 102442. https://doi.org/10.1016/j.techsoc.2023.102442
Lindgren, S. (2023). Critical theory of AI. John Wiley & Sons.
Lindgren, S., & Holmström, J. (2020). A social science perspective on artificial intelligence: Building blocks for a research agenda. Journal of Digital Social Research (JDSR), 2(3), 1-15. https://doi.org/10.33621/jdsr.v2i3.65
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1-35. https://doi.org/10.1145/3560815
Medvedev, A. N., Lambiotte, R., & Delvenne, J. C. (2019). The anatomy of Reddit: An overview of academic research. Dynamics on and of Complex Networks III: Machine Learning and Statistical Physics Approaches 10, 183-204.
Miller, C. A. (2003). The Etiquette Perspective for Human-Automation Relationships: Applications, Models and Results. Proceedings of the Human Factors and Ergonomics Society Annual Meeting,
Pan, C., Banerjee, J. S., De, D., Sarigiannidis, P., Chakraborty, A., & Bhattacharyya, S. (2023). ChatGPT: A OpenAI Platform for Society 5.0. Doctoral Symposium on Human Centered Computing,
Pokharel, R., Haghighi, P. D., Jayaraman, P. P., & Georgakopoulos, D. (2019, January). Analysing emerging topics across multiple social media platforms. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 1-9). https://doi.org/10.1145/3290688.3290720
Proferes, N., Jones, N., Gilbert, S., Fiesler, C., & Zimmer, M. (2021). Studying reddit: A systematic overview of disciplines, approaches, methods, and ethics. Social Media+ Society, 7(2), 20563051211019004. https://doi.org/10.1177/20563051211019004
Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems. https://doi.org/10.1016/j.iotcps.2023.04.003
Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare, 11(21), 2819. https://doi.org/10.3390/healthcare11060887
Schmidt, A., Giannotti, F., Mackay, W., Shneiderman, B., & Väänänen, K. (2021). Artificial intelligence for humankind: a panel on how to create truly interactive and Human-Centered AI for the benefit of individuals and Society. IFIP Conference on Human-Computer Interaction,
Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), 74-87. https://doi.org/10.4018/JDM.2020040105
Sloane, M., & Moss, E. (2019). AI’s social sciences deficit. Nature Machine Intelligence, 1(8), 330-331. https://doi.org/10.1038/s42256-019-0084-6
Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74-88. https://doi.org/10.1093/jcmc/zmz026
Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35. https://doi.org/10.3390/bdcc7010035
Thukral, S., Meisheri, H., Kataria, T., Agarwal, A., Verma, I., Chatterjee, A., & Dey, L. (2018, August). Analyzing behavioral trends in community driven discussion platforms like reddit. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 662-669). IEEE. https://doi.org/10.1109/ASONAM.2018.8508687
Van Pinxteren, M. M., Pluymaekers, M., & Lemmink, J. G. (2020). Human-like communication in conversational agents: a literature review and research agenda. Journal of Service Management, 31(2), 203-225.
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382
Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Education and Information Technologies, 1-25. https://doi.org/10.1007/s10639-023-11742-4
Yang, Q., Steinfeld, A., Rosé, C., & Zimmerman, J. (2020). Re-examining whether, why, and how human-AI interaction is uniquely difficult to design. Proceedings of the 2020 chi conference on human factors in computing systems.
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