# LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification

arXiv: https://arxiv.org/abs/2605.08448 · HTML: https://mlsanity.com/paper/2605.08448/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning](https://mlsanity.com/paper/2301.10921/) (2023)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics](https://mlsanity.com/paper/2406.15477/) (2024)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models](https://mlsanity.com/paper/2106.09204/) (2021)
- [DeCrisisMB: Debiased Semi-Supervised Learning for Crisis Tweet Classification via Memory Bank](https://mlsanity.com/paper/2310.14577/) (2023)
- [Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets](https://mlsanity.com/paper/1805.06289/) (2018)
- [Evaluating Robustness of LLMs on Crisis-Related Microblogs across Events, Information Types, and Linguistic Features](https://mlsanity.com/paper/2412.10413/) (2024)
- [Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks](https://mlsanity.com/paper/1608.03902/) (2016)
- [Zero-Shot Classification of Crisis Tweets Using Instruction-Finetuned Large Language Models](https://mlsanity.com/paper/2410.00182/) (2024)
- [Engage and Mobilize! Understanding Evolving Patterns of Social Media Usage in Emergency Management](https://mlsanity.com/paper/2501.15608/) (2025)
- [LLM-Guided Co-Training for Text Classification](https://mlsanity.com/paper/2509.16516/) (2025)

## Abstract (arXiv)

Semi-supervised learning approaches have been investigated as a means to enhance the analysis of social media data in disaster management contexts. In this work, we present the first empirical evaluation of large language model (LLM) guided semi-supervised learning for crisis related tweet classification. We compare two recent LLM assisted semi-supervised methods, VerifyMatch and LLM guided Co-Training ( LG-CoTrain), against established semi-supervised baselines. Our results show that LG-CoTrain significantly outperforms classical semi-supervised approaches in low resource settings with 5, 10 and 25 labeled examples per class, achieving the highest averaged Macro F1 across events. VerifyMatch achieves competitive performance while also demonstrating strong calibration properties. As the number of labeled examples increases, the performance gap narrows and Self Training emerges as a strong baseline. We further observe that compact semi-supervised models can, in some cases, outperform very large LLMs operating in zero-shot settings. This finding highlights the potential of transferring knowledge from LLMs into smaller and more deployable models through LLM guided semi-supervised learning, offering a practical pathway for real world disaster response applications. Our project repository on Github is here.