HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks

TL;DR

This paper introduces a large-scale, human-annotated Twitter dataset related to disaster events, along with a data collection pipeline and baseline classification results using deep learning models.

Contribution

It provides a new extensive dataset of 77,000 labeled tweets from 19 disaster events and a sampling pipeline, supporting advanced deep learning research.

Findings

Deep learning models outperform traditional methods on the dataset.

The dataset enables more accurate disaster-related tweet classification.

Baseline results establish a benchmark for future research.

Abstract

Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters. Despite its significantly large volume, social media content is often too noisy for direct use in any application. Therefore, it is important to filter, categorize, and concisely summarize the available content to facilitate effective consumption and decision-making. To address such issues automatic classification systems have been developed using supervised modeling approaches, thanks to the earlier efforts on creating labeled datasets. However, existing datasets are limited in different aspects (e.g., size, contains duplicates) and less suitable to support more advanced and data-hungry deep learning models. In this paper, we present a new large-scale dataset with ~77K human-labeled tweets, sampled from a pool of ~24 million tweets across 19 disaster events that happened between 2016 and 2019. Moreover, we propose a data collection and sampling pipeline, which is important for social media data sampling for human annotation. We report multiclass classification results using classic and deep learning (fastText and transformer) based models to set the ground for future studies. The dataset and associated resources are publicly available. https://crisisnlp.qcri.org/humaid_dataset.html