Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

TL;DR

This paper proposes a novel framework to extract actionable insights from social media discussions on domestic violence, aiming to support public health efforts and social welfare initiatives.

Contribution

It introduces a new framework for modeling and discovering themes related to domestic violence from social media data, addressing challenges of high-dimensional noisy datasets.

Findings

Framework effectively identifies DV-related themes

Enables public health insights from social media data

Supports social welfare decision-making

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.