Proceedings:
Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media
Volume
Issue:
Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media
Track:
Poster Papers
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Abstract:
During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situ- ational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised tech- nique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real- world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.
DOI:
10.1609/icwsm.v12i1.15047
ICWSM
Vol. 12 No. 1 (2018): Twelfth International AAAI Conference on Web and Social Media