# CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

arXiv: https://arxiv.org/abs/1805.00713 · HTML: https://mlsanity.com/paper/1805.00713/

## Key facts

- Cited by 89 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2018.

## Built on by (most cited first, 30 of 89)

- [Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey](https://mlsanity.com/paper/2411.17558/) (2024)
- [FedMultimodal: A Benchmark For Multimodal Federated Learning](https://mlsanity.com/paper/2306.09486/) (2023)
- [Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs](https://mlsanity.com/paper/2507.09477/) (2025)
- [HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks](https://mlsanity.com/paper/2104.03090/) (2021)
- [HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2504.12330/) (2025)
- [MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding](https://mlsanity.com/paper/2403.03077/) (2024)
- [Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications](https://mlsanity.com/paper/2302.00389/) (2023)
- [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)
- [Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space](https://mlsanity.com/paper/2402.16832/) (2024)
- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)
- [Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning](https://mlsanity.com/paper/2503.12972/) (2025)
- [A social context-aware graph-based multimodal attentive learning framework for disaster content classification during emergencies: a benchmark dataset and method](https://mlsanity.com/paper/2410.08814/) (2024)
- [Unleashing the Potential of Large Language Model: Zero-shot VQA for Flood Disaster Scenario](https://mlsanity.com/paper/2312.01882/) (2023)
- [Overcoming Language Disparity in Online Content Classification with Multimodal Learning](https://mlsanity.com/paper/2205.09744/) (2022)
- [CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization](https://mlsanity.com/paper/2210.14190/) (2022)
- [CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://mlsanity.com/paper/2309.05494/) (2023)
- [FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data](https://mlsanity.com/paper/2411.14717/) (2024)
- [CrisisKAN: Knowledge-infused and Explainable Multimodal Attention Network for Crisis Event Classification](https://mlsanity.com/paper/2401.06194/) (2024)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [CrisisViT: A Robust Vision Transformer for Crisis Image Classification](https://mlsanity.com/paper/2401.02838/) (2024)
- [MST-Distill: Mixture of Specialized Teachers for Cross-Modal Knowledge Distillation](https://mlsanity.com/paper/2507.07015/) (2025)
- [CrowdAgent: Multi-Agent Managed Multi-Source Annotation System](https://mlsanity.com/paper/2509.14030/) (2025)
- [Distilling Cross-Modal Knowledge via Feature Disentanglement](https://mlsanity.com/paper/2511.19887/) (2025)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability](https://mlsanity.com/paper/2205.11454/) (2022)
- [Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions](https://mlsanity.com/paper/2211.02646/) (2022)
- [NADBenchmarks -- a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters](https://mlsanity.com/paper/2212.10735/) (2022)
- [Can Text-to-image Model Assist Multi-modal Learning for Visual Recognition with Visual Modality Missing?](https://mlsanity.com/paper/2402.09036/) (2024)
- [Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data](https://mlsanity.com/paper/2202.00050/) (2022)

## Abstract (arXiv)

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.