sis 18(17): e2

Research Article

Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

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  • @ARTICLE{10.4108/eai.29-5-2018.154807,
        author={Sudha Subramani and Manjula O’Connor},
        title={Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={5},
        number={17},
        publisher={EAI},
        journal_a={SIS},
        year={2018},
        month={5},
        keywords={Domestic Violence, Pattern Mining, MapReduce, Topic Model, Actionable knowledge},
        doi={10.4108/eai.29-5-2018.154807}
    }
    
  • Sudha Subramani
    Manjula O’Connor
    Year: 2018
    Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media
    SIS
    EAI
    DOI: 10.4108/eai.29-5-2018.154807
Sudha Subramani1,*, Manjula O’Connor2
  • 1: Centre for Applied Informatics,Victoria University, Melbourne, Australia
  • 2: Department of Psychiatry, The University of Melbourne, Melbourne, Australia
*Contact email: sudha.subramani1@live.vu.edu.au

Abstract

Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sympathetic encouragement, to show compassion and empathy among public. But, it is difficult to mine the actionable knowledge from large conversational datasets from social media due to the characteristics of high dimensions, short, noisy, huge volume, high velocity, and so on. Hence, this paper will propose a novel framework to model and discover the various themes related to DV from the public domain. The proposed framework would possibly provide unprecedentedly valuable information to the public health researchers, national family health organizations, government and public with data enrichment and consolidation to improve the social welfare of the community. Thus provides actionable knowledge by monitoring and analysing continuous and rich user generated content.