Welcome to the InfoSci Platform
Reference Hub12
A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets

A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets

Reza Mazloom, Hongmin Li, Doina Caragea, Cornelia Caragea, Muhammad Imran
Copyright: © 2019 |Volume: 11 |Issue: 2 |Pages: 19
ISSN: 1937-9390|EISSN: 1937-9420|EISBN13: 9781522565376|DOI: 10.4018/IJISCRAM.2019070101
Cite Article Cite Article

MLA

Mazloom, Reza, et al. "A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets." IJISCRAM vol.11, no.2 2019: pp.1-19. http://doi.org/10.4018/IJISCRAM.2019070101

APA

Mazloom, R., Li, H., Caragea, D., Caragea, C., & Imran, M. (2019). A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets. International Journal of Information Systems for Crisis Response and Management (IJISCRAM), 11(2), 1-19. http://doi.org/10.4018/IJISCRAM.2019070101

Chicago

Mazloom, Reza, et al. "A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets," International Journal of Information Systems for Crisis Response and Management (IJISCRAM) 11, no.2: 1-19. http://doi.org/10.4018/IJISCRAM.2019070101

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Huge amounts of data generated on social media during emergency situations is regarded as a trove of critical information. The use of supervised machine learning techniques in the early stages of a crisis is challenged by the lack of labeled data for that event. Furthermore, supervised models trained on labeled data from a prior crisis may not produce accurate results, due to inherent crisis variations. To address these challenges, the authors propose a hybrid feature-instance-parameter adaptation approach based on matrix factorization, k-nearest neighbors, and self-training. The proposed feature-instance adaptation selects a subset of the source crisis data that is representative for the target crisis data. The selected labeled source data, together with unlabeled target data, are used to learn self-training domain adaptation classifiers for the target crisis. Experimental results have shown that overall the hybrid domain adaptation classifiers perform better than the supervised classifiers learned from the original source data.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.