[HTML][HTML] A systematic review of hate speech automatic detection using natural language processing

MS Jahan, M Oussalah - Neurocomputing, 2023 - Elsevier
With the multiplication of social media platforms, which offer anonymity, easy access and
online community formation and online debate, the issue of hate speech detection and …

Detecting harmful content on online platforms: what platforms need vs. where research efforts go

A Arora, P Nakov, M Hardalov, SM Sarwar… - ACM Computing …, 2023 - dl.acm.org
The proliferation of harmful content on online platforms is a major societal problem, which
comes in many different forms, including hate speech, offensive language, bullying and …

Culturellm: Incorporating cultural differences into large language models

C Li, M Chen, J Wang, S Sitaram… - Advances in Neural …, 2025 - proceedings.neurips.cc
Large language models (LLMs) have been observed to exhibit bias towards certain cultures
due to the predominance of training data obtained from English corpora. Considering that …

SemEval-2020 task 12: Multilingual offensive language identification in social media (OffensEval 2020)

M Zampieri, P Nakov, S Rosenthal, P Atanasova… - arxiv preprint arxiv …, 2020 - arxiv.org
We present the results and main findings of SemEval-2020 Task 12 on Multilingual
Offensive Language Identification in Social Media (OffensEval 2020). The task involves …

Hatebert: Retraining bert for abusive language detection in english

T Caselli, V Basile, J Mitrović, M Granitzer - arxiv preprint arxiv …, 2020 - arxiv.org
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …

Kuisail at semeval-2020 task 12: Bert-cnn for offensive speech identification in social media

A Safaya, M Abdullatif, D Yuret - arxiv preprint arxiv:2007.13184, 2020 - arxiv.org
In this paper, we describe our approach to utilize pre-trained BERT models with
Convolutional Neural Networks for sub-task A of the Multilingual Offensive Language …

Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages

T Mandl, S Modha, P Majumder, D Patel… - Proceedings of the 11th …, 2019 - dl.acm.org
The identification of Hate Speech in Social Media is of great importance and receives much
attention in the text classification community. There is a huge demand for research for …

ETHOS: a multi-label hate speech detection dataset

I Mollas, Z Chrysopoulou, S Karlos… - Complex & Intelligent …, 2022 - Springer
Online hate speech is a recent problem in our society that is rising at a steady pace by
leveraging the vulnerabilities of the corresponding regimes that characterise most social …

Overview of the hasoc subtrack at fire 2021: Hate speech and offensive content identification in english and indo-aryan languages and conversational hate speech

S Modha, T Mandl, GK Shahi, H Madhu… - Proceedings of the 13th …, 2021 - dl.acm.org
The HASOC track is dedicated to the evaluation of technology for finding Offensive
Language and Hate Speech. HASOC is creating a multilingual data corpus mainly for …

Multilingual offensive language identification with cross-lingual embeddings

T Ranasinghe, M Zampieri - arxiv preprint arxiv:2010.05324, 2020 - arxiv.org
Offensive content is pervasive in social media and a reason for concern to companies and
government organizations. Several studies have been recently published investigating …