Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data
This addresses scalability issues in computational social science for researchers analyzing social influence and information diffusion, though it appears incremental as it builds on existing methods like homophily and LLMs.
The paper tackles the challenge of modeling large-scale social network data for user detection tasks by integrating localized social interactions with large language models, achieving applicability across seven real-world datasets.
The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.