Arijit Ghosh Chowdhury, Ramit Sawhney, Rajiv Shah, Debanjan Mahata, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics :2527-2537 (2019).
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The availability of large-scale online social data, coupled with computational methods can help us answer fundamental questions relat-ing 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 at-tempts to aggregate such experiences of sex-ual abuse to facilitate a better understanding of social media constructs and to bring about social change. It has been found that disclo-sure of abuse has positive psychological im-pacts. Hence, we contend that such informa-tion can leveraged to create better campaigns for social change by analyzing how users react to these stories and to obtain a better insight into the consequences of sexual abuse. We use a three part Twitter-Specific Social Media Lan-guage Model to segregate personal recollec-tions of sexual harassment from Twitter posts. An extensive comparison with state-of-the-art generic and specific models along with a de-tailed error analysis explores the merit of our proposed model.