EVOLVE-X: Embedding Fusion and Language Prompting for User Evolution Forecasting on Social Media

TL;DR

This paper introduces a novel method combining open-source language models and joint embedding techniques to forecast user behavior evolution on social media, aiding in friend recommendations and activity predictions.

Contribution

It presents a new approach that integrates multiple language models with embedding techniques to predict social media user evolution over time.

Findings

GPT-2 achieved the lowest perplexity of 8.21 in predictions.

Cross-modal configurations outperform single-model setups.

The approach effectively forecasts future user network and activity changes.

Abstract

Social media platforms serve as a significant medium for sharing personal emotions, daily activities, and various life events, ensuring individuals stay informed about the latest developments. From the initiation of an account, users progressively expand their circle of friends or followers, engaging actively by posting, commenting, and sharing content. Over time, user behavior on these platforms evolves, influenced by demographic attributes and the networks they form. In this study, we present a novel approach that leverages open-source models Llama-3-Instruct, Mistral-7B-Instruct, Gemma-7B-IT through prompt engineering, combined with GPT-2, BERT, and RoBERTa using a joint embedding technique, to analyze and predict the evolution of user behavior on social media over their lifetime. Our experiments demonstrate the potential of these models to forecast future stages of a user's social evolution, including network changes, future connections, and shifts in user activities. Experimental results highlight the effectiveness of our approach, with GPT-2 achieving the lowest perplexity (8.21) in a Cross-modal configuration, outperforming RoBERTa (9.11) and BERT, and underscoring the importance of leveraging Cross-modal configurations for superior performance. This approach addresses critical challenges in social media, such as friend recommendations and activity predictions, offering insights into the trajectory of user behavior. By anticipating future interactions and activities, this research aims to provide early warnings about potential negative outcomes, enabling users to make informed decisions and mitigate risks in the long term.