Graph based semi-supervised learning with convolution neural networks to classify crisis related tweets

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

60 Citations (Scopus)

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 situational 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 technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specifically, 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.

Original languageEnglish
Title of host publication12th International AAAI Conference on Web and Social Media, ICWSM 2018
PublisherAAAI Press
Pages556-559
Number of pages4
ISBN (Electronic)9781577357988
Publication statusPublished - 2018
Event12th International AAAI Conference on Web and Social Media, ICWSM 2018 - Palo Alto, United States
Duration: 25 Jun 201828 Jun 2018

Publication series

Name12th International AAAI Conference on Web and Social Media, ICWSM 2018

Conference

Conference12th International AAAI Conference on Web and Social Media, ICWSM 2018
Country/TerritoryUnited States
CityPalo Alto
Period25/06/1828/06/18

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