This project analyzes social media conversations about ChatGPT through a combination of exploratory data analysis, network analysis, and natural language processing. The goal was to move beyond simple trending-topic observation and instead study how discussion communities formed, which topics dominated the conversation, and how sentiment varied across posts and participants.
The workflow followed three main stages. First, exploratory analysis was used to understand the dataset and the distribution of engagement features such as replies, reposts, likes, quotes, and hashtags. Second, the tweets were modeled as a network to examine centrality, clustering, modularity, and other structural patterns that reveal how discussion communities formed around the topic. Third, text analytics methods such as sentiment analysis, information extraction, named entity recognition, and topic modeling were applied to identify the most common themes and the most divisive areas of discussion.
The project was especially interesting because ChatGPT quickly became a cross-industry topic rather than something limited to a narrow technical audience. The analysis showed that conversations about it included influencers, technology companies, media accounts, and everyday users, making the topic suitable for both network analysis and text-based interpretation.
This work is still evolving, but it already demonstrates an analytical framework that can be reused for fast-moving public conversations: understand the data, model the network, then interpret the language. That combination is useful for studying social reaction, controversy, emerging communities, and topic diffusion around new technologies.