EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model

• Main Authors: Hossain, Ismail, Alam, Md Jahangir, Puppala, Sai, Talukder, Sajedul
• Format: Journal Article
• Language: English
• Published: 12.07.2024
• Subjects: Computer Science - Information Retrieval; Computer Science - Learning; Computer Science - Social and Information Networks
• DOI: 10.48550/arxiv.2407.09691

Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand their network of friends or followers, freely interacting with others by posting, commenting, and sharing content. Over time, user behavior evolves based on demographic attributes and the networks they establish. In this research, we propose a predictive method to understand how a user evolves on social media throughout their life and to forecast the next stage of their evolution. We fine-tune a GPT-like decoder-only model (we named it E-GPT: Evolution-GPT) to predict the future stages of a user's evolution in online social media. We evaluate the performance of these models and demonstrate how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media, which also addresses other social media challenges such as recommendation systems.