I-AID: Identifying Actionable Information from Disaster-related Tweets

TL;DR

This paper introduces I-AID, a multimodal system that automatically classifies disaster-related tweets into multiple information categories, improving the filtering of critical social media data during crises.

Contribution

The paper presents a novel multimodel approach combining BERT, GAT, and Relation Network for multi-label classification of disaster tweets, outperforming existing methods.

Findings

I-AID achieves +6% F1 score on TREC-IS dataset.

I-AID achieves +4% F1 score on COVID-19 Tweets.

Outperforms state-of-the-art in disaster tweet classification.

Abstract

Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT-based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-of-the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.