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Domestic violence crisis identification from Facebook posts based on deep learning

Subramani, Sudha, Wang, Hua, Vu, Huy Q. and Li, Gang 2018, Domestic violence crisis identification from Facebook posts based on deep learning, IEEE access, vol. 6, pp. 54075-54085, doi: 10.1109/ACCESS.2018.2871446.

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Title Domestic violence crisis identification from Facebook posts based on deep learning
Author(s) Subramani, Sudha
Wang, Hua
Vu, Huy Q.
Li, GangORCID iD for Li, Gang orcid.org/0000-0003-1583-641X
Journal name IEEE access
Volume number 6
Start page 54075
End page 54085
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018
ISSN 2169-3536
2169-3536
Summary OAPA Domestic Violence (DV) is a cause of concern due to the threat it poses towards public health and human rights. There is a need for quick identification of the victims of this condition, so that Domestic Violence Crisis Service (DVCS) can offer necessary support in a timely manner. The availability of social media has allowed DV victims to share their stories and receive support from community, which opens an opportunity for DVCS to actively approach and support DV victims. However, it is time consuming and inefficient to manually browse through a massive number of available posts. This paper adopts a Deep Learning as an approach for automatic identification of DV victims in critical need. Empirical evidence on a ground truth data set has achieved an accuracy of up to 94%, which outperforms traditional machine learning techniques. Analysis of informative features helps to identify important words which might indicate critical posts in the classification process. The experimental results are helpful to researchers and practitioners in developing techniques for identifying and supporting DV victims.
Language eng
DOI 10.1109/ACCESS.2018.2871446
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30114369

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.