EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model
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.
Cite
@article{arxiv.2407.09691,
title = {EVOLVE: Predicting User Evolution and Network Dynamics in Social Media Using Fine-Tuned GPT-like Model},
author = {Ismail Hossain and Md Jahangir Alam and Sai Puppala and Sajedul Talukder},
journal= {arXiv preprint arXiv:2407.09691},
year = {2024}
}
Comments
This article has been accepted as a long paper in the MSNDS 2024 workshop, to be held in conjunction with the International Conference on Social Networks Analysis and Mining (ASONAM 2024), September 2-5, 2024. and will be published in Springer