CrisisMMD: Multimodal Twitter Datasets from Natural Disasters
By Firoj Alam, Ferda Ofli, Muhammad Imran. 2018
432 citations recorded by the source corpus.
During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multimedia content to report updates about injured or dead people, infrastructure damage, missing or found people, among other information types. Studies have revealed that this online information, if processed timely and effectively, is extremely 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 from natural disasters collected from Twitter. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.
Subfields: Communication, Sociology and Political Science, Multimodal Learning, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
In Sociology and Political Science
In Multimodal Learning
- Davide Testuggine
- Pratik Ringshia
- Douwe Kiela
- Aravind Mohan
- Hamed Firooz
- Vedanuj Goswami
- Amanpreet Singh
- Ronghang Hu
In Evaluation & Benchmarks
- Douwe Kiela
- Vedanuj Goswami
- Davide Testuggine
- Hamed Firooz
- Amanpreet Singh
- Samuel R. Bowman
- Mohit Bansal
- Adina Williams
Citations in the corpus
- The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, methodology): “Other datasets using internet data include Food101 [80], where the goal is to predict the dish of recipes and images; various versions of Yelp reviews [52]; Walmart and Ferramenta product classification [90, 21]; social media name tagging (Twitter and Snapchat) [49]; social media target-oriented sen”
- MG2-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation (2026, unknown): “– Multimodal Classification: To assess real-world applicability, we evaluate MG 2 -RAG on CrisisMMD [1], a disaster-response dataset containing approximately 35k noisy social-media image-text pairs.”