This repository contains the implementation of I-AID approach and baseline methods discussed in the paper: I-AID: Identifying Actionable Information from Disaster-related Tweets. By Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif and Axel N.Ngnoga (DICE group, Department of Computer Science, Paderborn University)

This implementation is written in Python 3.6 and uses Tensorflow 2.0

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 categories. 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 multi-model approach to automatically categorize tweets into multi-label types and filter critical information from the enormous volume of social media data. We use Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to represent tweets into low-dimensional vectors. We thus employ a graph attention network to model the structural information between tweets tokens and their corresponding labels. 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-averaged F1-score by $+6%$ and $+4%$ on TREC-IS dataset and COVID19-Tweets respectively.

If you have any further questions/feedback, please contact corresponding author at [email protected]