# Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide

arXiv: https://arxiv.org/abs/2504.04346 · HTML: https://mlsanity.com/paper/2504.04346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [FineSurE: Fine-grained Summarization Evaluation using LLMs](https://mlsanity.com/paper/2407.00908/) (2024)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications](https://mlsanity.com/paper/2206.14358/) (2022)
- [Multi-rater Prompting for Ambiguous Medical Image Segmentation](https://mlsanity.com/paper/2404.07580/) (2024)
- [Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction](https://mlsanity.com/paper/2503.20981/) (2025)

## Built on by (most cited first, 1 of 1)

- [Structured Claim-Level Discourse Representations for Dense Health Narratives](https://mlsanity.com/paper/2609.18905/) (2026)

## Abstract (arXiv)

Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.