li-domesticviolencecrisis-2018.pdf (7.88 MB)
Domestic violence crisis identification from Facebook posts based on deep learning
journal contribution
posted on 2018-01-01, 00:00 authored by S Subramani, H Wang, Quan VuQuan Vu, Gang LiGang LiOAPA 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 accessVolume
6Pagination
54075 - 54085Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
Link to full text
ISSN
2169-3536eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2018, IEEEUsage metrics
Categories
Keywords
Science & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringDomestic violencedeep learningfeature extractionmachine learningsocial mediaSOCIAL SUPPORTHELP-SEEKINGSURVIVORSSERVICESVICTIMSInformation SystemsDistributed Computing
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