Child Abuse and Domestic Abuse: Content and Feature Analysis from Social Media Disclosures

Due to increase in popularity of social media, people have started discussing their thoughts and opinions in the form of textual posts. Currently, the people tend to disclose even the socially tabooed topics such as Child Abuse (CA), and Domestic Abuse (DA) to receive the desired response and social support in turn. The increasing volume of abuse related posts being shared on social media is of great interest for public health sectors and family welfare organizations to monitor the public health and promote support services. However, due to the large volume, high velocity and huge variety of context and content of user generated data, it is difficult to mine the different kinds of abuse (CA and DA) related posts from other general posts, that flood over the web. Hence, this paper aims to discover and differentiate the characteristics of CA and DA posts from the massive user generated posts, with the underlying context. Various features such as psycholinguistic, textual and sentimental features are analyzed and Machine Learning techniques are trained to analyze the predictive power of extracted features. Hence, the resulting model achieves more predictive power with high accuracy in classifying possible cases of abuse related posts from diverse user posts.