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- W3198000808 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." @default.
- W3198000808 created "2021-09-13" @default.
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- W3198000808 date "2021-01-01" @default.
- W3198000808 modified "2023-10-03" @default.
- W3198000808 title "I-AID: Identifying Actionable Information From Disaster-Related Tweets" @default.
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- W3198000808 doi "https://doi.org/10.1109/access.2021.3107812" @default.
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