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    <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, and the tasks of community detection, bot detection, and news media profiling.</abstract>
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%0 Conference Proceedings
%T Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media
%A Mehta, Nikhil
%A Goldwasser, Dan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F mehta-goldwasser-2024-using
%X 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, and the tasks of community detection, bot detection, and news media profiling.
%R 10.18653/v1/2024.findings-emnlp.309
%U https://aclanthology.org/2024.findings-emnlp.309/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.309
%P 5371-5390

Markdown (Informal)

[Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media](https://aclanthology.org/2024.findings-emnlp.309/) (Mehta & Goldwasser, Findings 2024)

• Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media (Mehta & Goldwasser, Findings 2024)

ACL

• Nikhil Mehta and Dan Goldwasser. 2024. Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5371–5390, Miami, Florida, USA. Association for Computational Linguistics.