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

TL;DR

This paper introduces E-GPT, a fine-tuned GPT-like model designed to predict user evolution and network dynamics in social media, enhancing understanding of user behavior changes and aiding recommendation systems.

Contribution

The paper presents a novel fine-tuning approach of GPT-like models to forecast user evolution and network changes in social media environments.

Findings

E-GPT accurately predicts future user connections and activity shifts.

User attributes significantly influence network evolution.

The model improves social media recommendation accuracy.

Abstract

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.