There are 3 repositories under offensive-language topic.
Repository for the paper "Automated Hate Speech Detection and the Problem of Offensive Language", ICWSM 2017
A fast, robust Python library to check for offensive language in strings.
✅ CODAR is a framework built using PyTorch to analyze post (Text+Media) and predict cyber bullying and offensive content.
NLP model that uses Machine Learning to detect offensive tweets, and classify it's target.
Contains code for a voting classifier that is part of an ensemble learning model for tweet classification (which includes an LSTM, a bayesian model and a proximity model) and a system for weighted voting
Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).
KAREN: Unifying Hatespeech Detection and Benchmarking
Disallow ⚠️ use of offensive 💪 language 💯
A Python package to compute HONEST, a score to measure hurtful sentence completions in language models. Published at NAACL 2021.
A GitHub action that monitors PR/issue comments and warns senders who used offensive language.
🤬🤭 Laravel validation rule that checks if a string is offensive.
Offend your friends and family! Generate scathing insults based on an edited dataset of insults scraped from various places around the web.
Dutch abusive language data
Code for replicating the results of "HateMonitors" at HASOC 2019
🤬🤭 Is Offensive Helper Function - Check if a string contains offensive words or variations of them
Arabic hate speech data
The most offensive of react components
Classifying hate speech with deep learning (honors thesis 2017-18)
This rule will validate that a field isn't offensive.
Turkish Hate Speech Detection
PHP library to look up information about words
CNN-based Twitter offensive text classification model
A speech act analysis of offensive language in German Tweets - an annotated datatset.
Arabic offensive speach detector with Arabic BERT and Pytorch
OffenseEval2020 Competetion
We utilized a pre-trained model to classify Arabic text. After conducting extensive research, we found that MarBERT was the best model for classifying Arabic offensive tweets. It focuses on dialectal Arabic (DA) and Modern Standard Arabic (MSA). The competition involves two shared sub-tasks: detecting whether a tweet is offensive or not; and detecting whether a tweet contains hate speech or not. It detected offensive sentences with 84.9% accuracy and F1-Score of 83.5%, and hate speech with 93.4% accuracy and F1-Score of 80.4%.
Offensive Language Identification and Categorization
Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals
A Django template filter that wraps around profanity-check
Hate Speech sentiment detection
This repository contains the system description and the codes that we implemented for participating in EACL-2021 shared tasks.
MS Thesis: Polarization in fake news and offensive language in Social Media - Twitter, Facebook, Blogs.
This is NLP project of text multi-classification. My pre-trained model classify speech into three different categories offensive, hateful and neither.