How can we detect Homophobia and Transphobia? Experiments in a multilingual code-mixed setting for social media governance

Abstract

Homophobia or Transphobia can be defined as the hatred, discomfort, or dislike of lesbian, gay, transgender or bisexual people. Studies have shown that these individuals were more likely to develop mental health issues, likely due to being subjected to more forms of abuse on social media. Hence there is an ardent need to develop automated abusive speech detection systems to tackle the abusive content on social media. There has been an elevation in hate speech or abuse and this paper focuses on the LGBTQIA+ community. Due to the shortage of resources in the said study area, we hypothesize that data augmentation via Pseudolabeling by transliterating the code-mixed text to the parent language will improve the models’ performances on the newly constructed dataset. We put our hypothesis into testing, and studied the performances of several multilingual language models for our cause.