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

Ismail Hossain, Md Jahangir Alam, Sai Puppala, Sajedul Talukder

TL;DR

A GPT-like decoder-only model is fine-tuneed to predict the future stages of a user's evolution in online social media and demonstrates how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media.

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.

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

TL;DR

A GPT-like decoder-only model is fine-tuneed to predict the future stages of a user's evolution in online social media and demonstrates how user attributes influence changes within their network by predicting future connections and shifts in user activities on social media.

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.
Paper Structure (19 sections, 12 equations, 3 figures, 7 tables)

This paper contains 19 sections, 12 equations, 3 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Research Objectives
  3. Literature Review
  4. Problem Formulation
  5. Motivation
  6. Methodology
  7. Adapting E-GPT Architecture for 2D Sequences
  8. Input Embedding
  9. Positional Encoding
  10. Transformer Decoder for GPT
  11. Reshaping Back to 2D
  12. Output Layer
  13. Experiment
  14. Dataset
  15. Model Fine-tune
  16. ...and 4 more sections

Figures (3)

  • Figure 1: (a) Autoregressive output from a decoder-only transformer architecture wolfe2022 (b) Temporal data a user in social media predicting the next stage of the evolution.
  • Figure 2: System architecture of user evolution in online social media
  • Figure 3: (a) Decoder language model architecture wolfe2023, (b) Our Decoder architecture