# QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language

arXiv: https://arxiv.org/abs/2008.12448 · HTML: https://mlsanity.com/paper/2008.12448/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media](https://mlsanity.com/paper/2101.11155/) (2021)
- [Overview of the HASOC Subtrack at FIRE 2022: Offensive Language Identification in Marathi](https://mlsanity.com/paper/2211.10163/) (2022)
- [Deep Learning for Bias Detection: From Inception to Deployment](https://mlsanity.com/paper/2110.15728/) (2021)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh](https://mlsanity.com/paper/2101.09667/) (2021)
- [HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments](https://mlsanity.com/paper/2312.13193/) (2023)
- [Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2506.23930/) (2025)

## Abstract (arXiv)

We describe our top-team solution to Task 1 for Hindi in the HASOC contest organised by FIRE 2019. The task is to identify hate speech and offensive language in Hindi. More specifically, it is a binary classification problem where a system is required to classify tweets into two classes: (a) Hate and Offensive (HOF) and (b) Not Hate or Offensive (NOT). In contrast to the popular idea of pretraining word vectors (a.k.a. word embedding) with a large corpus from a general domain such as Wikipedia, we used a relatively small collection of relevant tweets (i.e. random and sarcasm tweets in Hindi and Hinglish) for pretraining. We trained a Convolutional Neural Network (CNN) on top of the pretrained word vectors. This approach allowed us to be ranked first for this task out of all teams. Our approach could easily be adapted to other applications where the goal is to predict class of a text when the provided context is limited.