Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media

TL;DR

This paper proposes a novel approach using reinforcement learning to enhance large language models' ability to identify social media communities, demonstrating improved performance on Reddit and Twitter data for various community-related tasks.

Contribution

It introduces a method to train smaller models to better prompt large language models, improving community detection capabilities in social media analysis.

Findings

Improved community detection accuracy on Reddit and Twitter datasets.

Enhanced bot and news media profiling performance.

Effective strategies for training smaller models to guide LLMs.

Abstract

The large scale usage of social media, combined with its significant impact, has made it increasingly important to understand it. In particular, identifying user communities, can be helpful for many downstream tasks. However, particularly when models are trained on past data and tested on future, doing this is difficult. In this paper, we hypothesize to take advantage of Large Language Models (LLMs), to better identify user communities. Due to the fact that many LLMs, such as ChatGPT, are fixed and must be treated as black-boxes, we propose an approach to better prompt them, by training a smaller LLM to do this. We devise strategies to train this smaller model, showing how it can improve the larger LLMs ability to detect communities. Experimental results show improvements on Reddit and Twitter data, on the tasks of community detection, bot detection, and news media profiling.