Extracting Actionable Knowledge from Domestic Violence Discourses on Social Media

Abstract

Respect for human rights is the cornerstone of strong communities, based on the principles of dignity, equality, and recognition of inherent value of each individual. Domestic Violence, ranging from physical abuse to emotional manipulation, is worldwide considered as the violation of the elementary rights to which all human beings are entitled to. As one might expect, the consequences for its victims are often severe, far-reaching, and long-lasting, causing major health, welfare, and economic burden. Domestic Violence is also one of the most prevailing forms of violence, and due to the social stigma surrounding the issue particularly challenging to address. With the emergence and expansion of Social Media, the substantial shift in the support-seeking and the support-provision pattern has been observed. The initial barriers in approaching healthcare professionals, i.e. personal reservations, or safety concerns, have been effectively addressed by virtual environments. Social Media platforms have quickly become crucial networking hubs for violence survivors as well as at-risk individuals to share experiences, raise concerns, offer advice, or express sympathy. As a result, the specialized support services groups have been established with the aim of pro-active reach-out to potential victims in time-critical situations. Given the high-volume, highvelocity and high-variety of Social Media data, the manual posts evaluation has not only become inefficient, but also unfeasible in the long-term. The conventional automated approaches reliant on pre-defined lexicons, and hand-crafted feature engineering proved limited in their classification performance capability when exposed to the challenging nature of Social Media discourse. At the same time, Deep Learning the state-of-the-art sub-field of Machine Learning has shown remarkable results on text classification tasks. Given its relative recency and algorithmical complexity, the implementation of Deep Learningbased models has been vastly under-utilised in practical applications. In particular, no prior work has addressed the problem of fine-grained user-generated content classification with Deep Learning in Domestic Violence domain. The study introduces novel 3-part framework aimed at (i) binary detection of critical situations; (ii) multi-class content categorization; and (ii) Abuse Types and Health Issues extraction from Social Media discourse. The classification performance of state-of-the-art models is improved through the domain-specific word embeddings development, capable of precise relationships between the words recognition. The prevalent patterns of abuse, and the associated health conditions are efficiently extracted to shed the light on violence scale and severity from directly affected individuals. The approach proposed marks a step forward towards effective prevention and mitigation of violence within the society.