Large Language Models for Social Networks: Applications, Challenges, and Solutions

TL;DR

This paper explores the development of Large Language Model applications for social networks, categorizing tasks, discussing challenges, solutions, and lessons learned, filling a research gap in this emerging area.

Contribution

It provides the first comprehensive analysis of LLM applications in social networks, including categorization, challenges, solutions, and practical insights.

Findings

Identified three main categories of LLM tasks in social networks

Developed solutions for challenges in knowledge, entertainment, and moderation tasks

Shared lessons learned from implementing LLMs in social network contexts

Abstract

Large Language Models (LLMs) are transforming the way people generate, explore, and engage with content. We study how we can develop LLM applications for online social networks. Despite LLMs' successes in other domains, it is challenging to develop LLM-based products for social networks for numerous reasons, and it has been relatively under-reported in the research community. We categorize LLM applications for social networks into three categories. First is knowledge tasks where users want to find new knowledge and information, such as search and question-answering. Second is entertainment tasks where users want to consume interesting content, such as getting entertaining notification content. Third is foundational tasks that need to be done to moderate and operate the social networks, such as content annotation and LLM monitoring. For each task, we share the challenges we found, solutions we developed, and lessons we learned. To the best of our knowledge, this is the first comprehensive paper about developing LLM applications for social networks.