Social media data plays a significant role in modern disaster management by providing valuable data about affected people, donations , help requests, and advice . Recent studies highlight the need to filter information on social media into fine-grained content categories . However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. Automatically categorizing the information (e.g., reports on affected individuals, donations, and volunteers) contained in these posts is vital for their efficient handling and consumption by the communities affected URLanizations concerned. In this paper, we propose a system, dubbed I-AID, to automatically filter tweets with critical or actionable information from the enormous volume of social media data. Our system combines state-of-the-art approaches to process and represents textual data in order to capture its underlying semantics . In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in tweets and their corresponding labels . We conducted our experiments on a real-world dataset of disaster-related tweets. Our experimental results indicate that our model outperforms state-of-the-art approaches baselines in terms of F1-score by + 11\% .
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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data
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modern
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, help requests, and advice
and help requests
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categories
labels
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Automatically categorizing the information (e.g., reports on affected individuals, donations, and volunteers) contained in these posts is vital for their efficient handling and consumption by the communities affected URLanizations concerned.
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a system, dubbed
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to automatically filter tweets with critical or actionable
a multimodel approach to automatically categorize tweets into multi-label information types and filter critical
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Our system combines state-of-the-art approaches to process and represents textual data in order to capture its underlying semantics . In particular, we use 1) Bidirectional Encoder Representations from Transformers (commonly known as, BERT) to learn a contextualized vector representation of a tweet, and 2) a graph-based architecture to compute semantic correlations between the entities and hashtags in
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
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labels
information types in a supervised way
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our experiments on a real-world dataset of disaster-related tweets. Our experimental
several experiments on two real publicly-available datasets. Our
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our model
I-AID
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baselines
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F1-score
weighted average F1 score
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11\%
6\% and +4\% on the TREC-IS dataset and COVID-19 Tweets, respectively