I-AID: Identifying Actionable Information From Disaster-Related Tweets

• Main Authors: Hamada M. Zahera, Rricha Jalota, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Crisis information; contextualized text embedding; social media analysis; graph attention network; meta-learning
• Online Access: https://ieeexplore.ieee.org/document/9522108/

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&#x2019; words/entities and the corresponding information types, and iii) a <italic>Relation Network</italic> 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 &#x002B;6&#x0025; and &#x002B;4&#x0025; on the TREC-IS dataset and COVID-19 Tweets, respectively.