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

H.M.A. Zahera, R. Jalota, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE Open Access, 2021.

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Conference Paper | English
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.
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Zahera HMA, Jalota R, Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable Information from Disaster-related Tweets. In: IEEE Open Access. ; 2021.
Zahera, H. M. A., Jalota, R., Sherif, M., & Ngonga Ngomo, A.-C. (2021). I-AID: Identifying Actionable Information from Disaster-related Tweets. IEEE Open Access.
@inproceedings{Zahera_Jalota_Sherif_Ngonga Ngomo_2021, title={I-AID: Identifying Actionable Information from Disaster-related Tweets}, booktitle={IEEE Open Access}, author={Zahera, Hamada Mohamed Abdelsamee and Jalota, Rricha and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2021} }
Zahera, Hamada Mohamed Abdelsamee, Rricha Jalota, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” In IEEE Open Access, 2021.
H. M. A. Zahera, R. Jalota, M. Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying Actionable Information from Disaster-related Tweets,” 2021.
Zahera, Hamada Mohamed Abdelsamee, et al. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” IEEE Open Access, 2021.

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