LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media

ACL ARR 2025 February Submission6753 Authors

Abstract: With the vast expansion of content on social media platforms, analyzing and comprehending online discourse has become increasingly complex. This paper introduces LLMTaxo, a novel framework leveraging large language models for the automated construction of taxonomy of factual claims from social media by generating topics from multi-level granularities. This approach aids stakeholders in more effectively navigating the social media landscapes. We implement this framework with different models across three distinct datasets and introduce specially designed taxonomy evaluation metrics for a comprehensive assessment. With the evaluations from both human evaluators and GPT-4, the results indicate that LLMTaxo effectively categorizes factual claims from social media, and reveals that certain models perform better on specific datasets.

Paper Type: Long

Research Area: Computational Social Science and Cultural Analytics

Research Area Keywords: Computational Social Science and Cultural Analytics; Generation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 6753