A literature review of textual hate speech detection methods and datasets
F Alkomah, X Ma - Information, 2022 - mdpi.com
Online toxic discourses could result in conflicts between groups or harm to online
communities. Hate speech is complex and multifaceted harmful or offensive content …
communities. Hate speech is complex and multifaceted harmful or offensive content …
Machine learning techniques for hate speech classification of twitter data: State-of-the-art, future challenges and research directions
Twitter is a microblogging tool that allow the creation of big data through short digital
contents. This study provides a survey of machine learning techniques for hate speech …
contents. This study provides a survey of machine learning techniques for hate speech …
Hatebert: Retraining bert for abusive language detection in english
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 …
detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit …
Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval)
We present the results and the main findings of SemEval-2019 Task 6 on Identifying and
Categorizing Offensive Language in Social Media (OffensEval). The task was based on a …
Categorizing Offensive Language in Social Media (OffensEval). The task was based on a …
BanglaHateBERT: BERT for abusive language detection in Bengali
This paper introduces BanglaHateBERT, a retrained BERT model for abusive language
detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive …
detection in Bengali. The model was trained with a large-scale Bengali offensive, abusive …
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements
As generative large model capabilities advance, safety concerns become more pronounced
in their outputs. To ensure the sustainable growth of the AI ecosystem, it's imperative to …
in their outputs. To ensure the sustainable growth of the AI ecosystem, it's imperative to …
Detection of hate: speech tweets based convolutional neural network and machine learning algorithms
HA Sennary, G Abozaid, A Hemeida, A Mikhaylov… - Scientific Reports, 2024 - nature.com
There is no doubt that social media sites have provided many benefits to humanity, such as
sharing information continuously and communicating with others easily. It also seems that …
sharing information continuously and communicating with others easily. It also seems that …
Automatic hate speech detection: A literature review
M Mohiyaddeen, S Siddiqi - Available at SSRN 3887383, 2021 - papers.ssrn.com
Hate speech has been an ongoing problem on the Internet for many years. Besides, social
media, especially Facebook, and Twitter have given it a global stage where those hate …
media, especially Facebook, and Twitter have given it a global stage where those hate …
LOD-connected offensive language ontology and tagset enrichment
B Lewandowska-Tomaszczyk, S Žitnik… - … : Proceedings of the …, 2021 - cris.mruni.eu
The main focus of the paper is the definitional revision and enrichment of offensive language
typology, making reference to publicly available offensive language datasets and testing …
typology, making reference to publicly available offensive language datasets and testing …
Astartwice at semeval-2021 task 5: Toxic span detection using roberta-crf, domain specific pre-training and self-training
TA Suman, A Jain - … of the 15th International Workshop on …, 2021 - aclanthology.org
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our
solution is built upon RoBERTa language model and Conditional Random Fields (CRF). We …
solution is built upon RoBERTa language model and Conditional Random Fields (CRF). We …