Breaking the Silence Detecting and Mitigating Gendered Abuse in Hindi, Tamil, and Indian English Online Spaces

TL;DR

This work tackles automatic detection of gendered harassment in Hindi, Tamil, and Indian English, addressing data scarcity and linguistic diversity in Indic languages. It adopts a CNN-BiLSTM ensemble with non-trainable pretrained embeddings to capture local and sequential text patterns, and extends its effectiveness through transfer learning using MACD and MULTILATE datasets. The method achieves strong F1 scores across three tasks in the ICON2023 shared task, with English showing the strongest validation performance and Task 2 benefiting most from external data. The study highlights the practicality of scalable, language-aware detection of cyber harassment in Indic spaces and contributes open-source data and code to spur further research.

Abstract

Online gender-based harassment is a widespread issue limiting the free expression and participation of women and marginalized genders in digital spaces. Detecting such abusive content can enable platforms to curb this menace. We participated in the Gendered Abuse Detection in Indic Languages shared task at ICON2023 that provided datasets of annotated Twitter posts in English, Hindi and Tamil for building classifiers to identify gendered abuse. Our team CNLP-NITS-PP developed an ensemble approach combining CNN and BiLSTM networks that can effectively model semantic and sequential patterns in textual data. The CNN captures localized features indicative of abusive language through its convolution filters applied on embedded input text. To determine context-based offensiveness, the BiLSTM analyzes this sequence for dependencies among words and phrases. Multiple variations were trained using FastText and GloVe word embeddings for each language dataset comprising over 7,600 crowdsourced annotations across labels for explicit abuse, targeted minority attacks and general offences. The validation scores showed strong performance across f1-measures, especially for English 0.84. Our experiments reveal how customizing embeddings and model hyperparameters can improve detection capability. The proposed architecture ranked 1st in the competition, proving its ability to handle real-world noisy text with code-switching. This technique has a promising scope as platforms aim to combat cyber harassment facing Indic language internet users. Our Code is at https://github.com/advaithavetagiri/CNLP-NITS-PP

Figures (3)

• Figure 1: CNN-BiLSTM Architecture for Sexism Classification
• Figure 2: CNN-BiLSTM Model Summary
• Figure 3: Task 2 Accuracy and Loss of English (Top), Hindi (Middle) and Tamil(Bottom).