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

• URL: http://arxiv.org/abs/2008.13544v2
• Date: Wed, 19 May 2021 02:32:43 GMT
• Title: I-AID: Identifying Actionable Information from Disaster-related Tweets
• Authors: Hamada M. Zahera, Rricha Jalota, Mohamed A. Sherif, Axel N. Ngomo
• Abstract summary: Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. We propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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