Author:
Nguyen Dat,Ali Al Mannai Kamela,Joty Shafiq,Sajjad Hassan,Imran Muhammad,Mitra Prasenjit
Abstract
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. The scarcity of labeled data, particularly in the early hours of a crisis, delays the learning process. Existing classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for identifying useful tweets during a crisis situation. At the onset of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
38 articles.
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