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


As the increasing volume of abuse related posts shared on social media is of interest for the public health sector and family welfare organisations to monitor public health, this study aims to identify such posts and differentiate between child abuse and domestic abuse. Researchers first analysed psycholinguistic, textual and somatic features in social media posts disclosing child abuse and domestic abuse in order find out what characterises such posts, and then deployed machine learning classifiers to examine the extracted features’ predictive power. The abuse related posts had higher proportions for features such as anxiety, anger, sadness, sexual health, and death, and carried a lot of negative emotion.

Artificial intelligenceMachine learningPreventionChild Sexual Abuse Material (CSAM)
Research (peer reviewed)