  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.
