{"author":[{"orcid":"0000-0003-0215-1278","first_name":"Hamada Mohamed Abdelsamee","last_name":"Zahera","id":"72768","full_name":"Zahera, Hamada Mohamed Abdelsamee"},{"first_name":"Rricha","full_name":"Jalota, Rricha","id":"69526","last_name":"Jalota"},{"first_name":"Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","full_name":"Sherif, Mohamed","id":"67234","last_name":"Sherif"},{"id":"65716","last_name":"Ngonga Ngomo","full_name":"Ngonga Ngomo, Axel-Cyrille","first_name":"Axel-Cyrille"}],"abstract":[{"text":"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.","lang":"eng"}],"status":"public","year":"2021","user_id":"67234","citation":{"short":"H.M.A. Zahera, R. Jalota, M. Sherif, A.-C. Ngonga Ngomo, in: IEEE Open Access, 2021.","apa":"Zahera, H. M. A., Jalota, R., Sherif, M., &#38; Ngonga Ngomo, A.-C. (2021). I-AID: Identifying Actionable Information from Disaster-related Tweets. <i>IEEE Open Access</i>.","chicago":"Zahera, Hamada Mohamed Abdelsamee, Rricha Jalota, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” In <i>IEEE Open Access</i>, 2021.","bibtex":"@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} }","mla":"Zahera, Hamada Mohamed Abdelsamee, et al. “I-AID: Identifying Actionable Information from Disaster-Related Tweets.” <i>IEEE Open Access</i>, 2021.","ama":"Zahera HMA, Jalota R, Sherif M, Ngonga Ngomo A-C. I-AID: Identifying Actionable Information from Disaster-related Tweets. In: <i>IEEE Open Access</i>. ; 2021.","ieee":"H. M. A. Zahera, R. Jalota, M. Sherif, and A.-C. Ngonga Ngomo, “I-AID: Identifying Actionable Information from Disaster-related Tweets,” 2021."},"date_created":"2021-12-17T10:06:30Z","keyword":["sys:relevantFor:infai sys:relevantFor:DAIKIRI ngonga zahera sherif daikiriproject dice simba"],"_id":"29043","title":"I-AID: Identifying Actionable Information from Disaster-related Tweets","language":[{"iso":"eng"}],"type":"conference","date_updated":"2023-08-16T09:35:42Z","publication":"IEEE Open Access"}