Graph Based Semi-Supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
Year: 2018
Type: conference-paper
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.
Authors: Firoj Alam, Shafiq Joty, Muhammad Imran
Institution: Nanyang Technological University
Language: English
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W2804079529