  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.
