From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling

TL;DR

This paper presents a novel LLM-based user profiling method for social media that produces interpretable profiles with minimal labeled data, outperforming existing techniques in flexibility and accuracy.

Contribution

Introduces a two-stage LLM-based approach using domain-defining statements for adaptable, interpretable user profiling with minimal supervision.

Findings

Outperforms state-of-the-art methods by 9.8%

Generates natural language profiles that are interpretable

Reduces reliance on large labeled datasets

Abstract

Social media user profiling through content analysis is crucial for tasks like misinformation detection, engagement prediction, hate speech monitoring, and user behavior modeling. However, existing profiling techniques, including tweet summarization, attribute-based profiling, and latent representation learning, face significant limitations: they often lack transferability, produce non-interpretable features, require large labeled datasets, or rely on rigid predefined categories that limit adaptability. We introduce a novel large language model (LLM)-based approach that leverages domain-defining statements, which serve as key characteristics outlining the important pillars of a domain as foundations for profiling. Our two-stage method first employs semi-supervised filtering with a domain-specific knowledge base, then generates both abstractive (synthesized descriptions) and extractive (representative tweet selections) user profiles. By harnessing LLMs' inherent knowledge with minimal human validation, our approach is adaptable across domains while reducing the need for large labeled datasets. Our method generates interpretable natural language user profiles, condensing extensive user data into a scale that unlocks LLMs' reasoning and knowledge capabilities for downstream social network tasks. We contribute a Persian political Twitter (X) dataset and an LLM-based evaluation framework with human validation. Experimental results show our method significantly outperforms state-of-the-art LLM-based and traditional methods by 9.8%, demonstrating its effectiveness in creating flexible, adaptable, and interpretable user profiles.