Abstract The availability of large-scale online social data, coupled with computational methods, can help us answer fundamental questions relating to our social lives, particularly our health and well-being. The #MeToo trend has led to people talking about personal experiences of harassment more openly. This work attempts to aggregate such experiences of sexual abuse to facilitate a better understanding of social media constructs and to bring about social change. It has been found that disclosure of abuse has positive psychological impacts. Hence, we contend that such information can be leveraged to create better campaigns for social change by analyzing how users react to these stories and can be used to obtain a better insight into the consequences of sexual abuse. We use a three-part TwitterSpecific Social Media Language Model to segregate personal recollections of sexual harassment from Twitter posts. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis explores the merit of our proposed model.