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2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) by Krishnan, Jitin (author);Purohit, Hemant (author);Rangwala, Huzefa (author) is a nonfiction available to read on EtoBox.

What is 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) about?

During the onset of a natural or man-made crisis event, public often share relevant information for emergency services on social web platforms such as Twitter. However, filtering such relevant data in real-time at scale using social media mining is challenging due to the short noisy text, sparse availability of relevant data, and also, practical limitations in collecting large labeled data during an ongoing event. In this paper, we hypothesize that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events for training efficient information filtering models during the sudden onset of a new crisis. We present a novel method to classify relevant social posts during an ongoing crisis without seeing any new data from this event (fully unsupervised domain adaptation). Specifically, we construct a customized multi-task architecture with a multi-domain discriminator for crisis analytics: multi-task domain adversarial attention network (MT-DAAN). This model consists of dedicated attention layers for each task to provide model interpretability; critical for real-word applications. As deep networks struggle with sparse datase

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Author

Krishnan, Jitin (author);Purohit, Hemant (author);Rangwala, Huzefa (author)

Publisher

IEEE

Published

2020

Language

EN

Category

nonfiction