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)
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.
python 3.6
tensorflow 2.0
spaCy 3.0
NLTK 3.6.2
scikit-learn 0.24
pickle5 0.0.11
You can install all requirements via: pip install -r requirements.txt
We conducted our experiments on two public datasets provided by TREC (TREC-IS 2019 edition and COVID-19 Tweets):
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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.
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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).
We provide descriptive notebooks for our approach and baseline methods in notebook folder. We share also our implementation publicly on Google colab server.
Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif and Axel N.Ngnoga (DICE group, Department of Computer Science, Paderborn University)
If you have any further questions/feedback, please contact corresponding author at hamada.zahera@uni-paderborn.de