Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversations
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Abstract (arXiv)
The role of social media in information dissemination and agenda-setting has significantly expanded in recent years. By offering real-time interactions, online platforms have become invaluable tools for studying societal responses to significant events as they unfold. However, online reactions to external developments are influenced by various factors, including the nature of the event and the online environment. This study examines the dynamics of public discourse on digital platforms to shed light on this issue. We analyzed over 12 million posts and news articles related to two significant events: the release of ChatGPT in 2022 and the global discussions about COVID-19 vaccines in 2021. Data was collected from multiple platforms, including Twitter, Facebook, Instagram, Reddit, YouTube, and GDELT. We employed topic modeling techniques to uncover the distinct thematic emphases on each platform, which reflect their specific features and target audiences. Additionally, sentiment analysis revealed various public perceptions regarding the topics studied. Lastly, we compared the evolution of engagement across platforms, unveiling unique patterns for the same topic. Notably, discussions about COVID-19 vaccines spread more rapidly due to the immediacy of the subject, while discussions about ChatGPT, despite its technological importance, propagated more gradually.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Oct 2023.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 1.
5 papers.
- 2023Does GPT-4 surpass human performance in linguistic pragmatics?cited by 16
- 2024Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccinationcited by 5
- 2025Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Contextcited by 2
- 2026LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humanscited by 1
- 2025Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Frameworkcited by 0
- 2026LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans31 Mar 2026
- 2025Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context3 Dec 2025
- 2025Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework20 Aug 2025
- 2024Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination10 Apr 2024
- 2023Does GPT-4 surpass human performance in linguistic pragmatics?15 Dec 2023