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

This repository contains the source-code and datasets of the I-AID approach discussed in: 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)

Summary:

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.

Dependencies:

python 3.6
tensorflow 2.0
spaCy 3.0
NLTK 3.6.2 
scikit-learn 0.24
pickle5 0.0.11

Installation:

You can install all requirements via: pip install -r requirements.txt

Dataset:

We conducted our experiments on two public datasets provided by TREC (TREC-IS 2019 edition and COVID-19 Tweets):

  • TREC-IS: this dataset contains approximately 35K tweets collected during 33 different disasters between 2012 and 2019 (e.g., wildfires, earthquakes, hurricanes, bombings,} and floods). The tweets are labeled with 25 information types by human experts and volunteers.

  • COVID-19 Tweets: this dataset contains a collection of tweets about the COVID-19 outbreak in different affected regions. In total, the data has 7,5k tweets labeled with one or more of the full 12 information type labels (the same as for the TREC-IS dataset).

Run the code:

We provide descriptive notebooks for our approach and baseline methods in notebook folder. We share also our implementation publicly on Google colab server.

Cite:

Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif and Axel N.Ngnoga (DICE group, Department of Computer Science, Paderborn University)

Contact:

If you have any further questions/feedback, please contact corresponding author at hamada.zahera@uni-paderborn.de