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Domestic violence crisis identification from Facebook posts based on deep learning

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journal contribution
posted on 2018-01-01, 00:00 authored by S Subramani, H Wang, Quan VuQuan Vu, Gang LiGang Li
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.

History

Journal

IEEE access

Volume

6

Pagination

54075-54085

Location

Piscataway, N.J.

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE

Publisher

Institute of Electrical and Electronics Engineers