Hate-speech and offensive language detection model using various Machine Learning and NLP techniques and labeled Twitter data

Recurrent Neural Network based Hate Speech Language Model for Korean Hate Speech Detection

Bert language model for hate speech detection.

The project focuses on Explainable AI (XAI) by utilizing Large Language Models (LLMs) and Chain-of-Thought (CoT) prompting to not only classify hate speech but also extract rationales and implied statements. We leverage the Qwen2.5-3B model fine-tuned with QLoRA to achieve state-of-the-art performance.

Ml model to detect hate speech and offensive language

The library integrates voice-based offensive content detection in iOS apps, utilizing Apple's Speech framework and a machine learning model created with Create ML. It accurately identifies offensive language and hate speech, supporting both SwiftUI and UIKit for content moderation.

With the increase in user-generated content on social media networks, hate speech and offensive language content are also increasing. From the perspective of computer science, automatic detection of such hate speech and offensive language content is an interesting problem to solve. The natural language community has taken a step to identify such content via automated hate speech and offensive content detection. The hate speech content is generated mostly on social media, and automatic hate speech and offensive language detection face many challenges due to non-standard spelling and grammar variations. Specifically, in a multilingual community, the hate content would be in code-mixed form, making the task further challenging. In this article, we propose a model for code-mixed hate speech detection. This model embeds the knowledge from both user-trained and multilingual pre-trained models. The proposed method also calculates the profanity word list and augments it. Experimental results on code-mixed hate speech and offensive language detection benchmarks show that our method outperforms the existing baselines.

Dockerized basic tweet classifier app. Hate speech and offensive language detection model using various Machine Learning and NLP techniques. Also, Hate Speech Detection for tweets with k8s Cluster

Code for the paper: Towards Interpretable Hate Speech Detection using Large Language Model-extracted Rationales, accepted at NAACL WOAH 2024

A Multi-task learning model for 5 classification tasks on Hate Speech dataset with three languages (Arabic, English, French). The model is based on sluice network (Sluice Network model (https://arxiv.org/abs/1705.08142).

Official implementation of the paper "ConPrompt: Pre-training a Language Model with Machine-Generated Data for Implicit Hate Speech Detection" (Findings of EMNLP 2023)

A DistillBert model that can detect hate speech in 7 different languages

MFTCXplain is the first multilingual benchmark dataset designed to evaluate the moral reasoning of Large Language Models (LLM) through multi-hop hate speech explanations grounded in Moral Foundations Theory (MFT).

Sensitive Content Moderation using BERT employs a deep learning model to detect and filter offensive, harmful, or inappropriate content online. By understanding context and meaning in text, BERT enhances accuracy in identifying hate speech, explicit language, or abuse, promoting safer communication on digital platforms.

Automated recognition and detection of Hate Speech and Offensive language on different Online Social Networks, mainly Twitter, presents a challenge to the community of Artificial Intelligence and Machine Learning. Unfortunately, sometimes these ideas communicated via the internet are intended to promote or incite hatred or humiliation of an individual, community, or even organizations. The HASOC shared task is to attempt to automatically detect abusive language on Twitter in English and Indo-Aryan Languages like Hindi. To participate in this task and provide our input, we (team Data Pirates) presented several machine learning models for Hindi Subtasks. The datasets provided allowed the development and testing of supervised machine learning techniques. The top 2 performing models for sub-task A were Naïve Bayes and Logistic Regression with the same Macro F1 score of 0.7394. The top 2 performing models for sub-task B were Logistic Regression and CatBoost, with Macro F1 scores of 0.4828 and 0.4709, respectively. This overview intends to provide detailed understandings and to analyze the outcomes.

Hate-speech and offensive language detection model using various Machine Learning and NLP techniques and labeled Twitter data

Decoding Hate: Exploring Language Models' Reactions to Hate Speech @ NAACL '25

NLP Model for analyzing hate speech in social media in Albanian language

Transformers models for Hate Speech and Offensive Language Detection on Arabic Twitter

Personalisation or Prejudice? Addressing Geographic Bias in Hate Speech Detection using Debias Tuning in Large Language Models @ ICWSM '26

🚨 A complete NLP pipeline to detect hate speech, offensive language, and neutral content using TF-IDF and machine learning. Includes EDA, preprocessing, model training, evaluation, and a reusable Python script for prediction.

SafeSocial: Advanced toxicity detection models for tweets. Utilizes state-of-the-art machine learning techniques to identify toxic language, hate speech, and abusive content. Empowering online communities with safer digital interactions. 🚀 #NLP #MachineLearning #ToxicityDetection

AI model(TFIDF) that detects hate speech in Georgian language

Project of the thesis: From Individuals to Communities: Community-Aware Language Modeling for the Detection of Hate Speech

Hate Speech Detection: Textual hate speech detection identifies and categorizes hate speech in written content. These models, which include machine learning and deep learning algorithms, are trained on labelled data to differentiate between hate speech, offensive language and non-offensive information.

A project to classify the input text as hate speech or not using an LSTM model trained on the Hate Speech and Offensive Language dataset and Twitter hate speech dataset from Kaggle.

Machine learning model detect hate speech and offensive language through instant messaging on Discord

Classification of hate speech and implicitness of hate speech, using Transformer language models (BERT). This repository can be used as an introduction to text classification with BERT-like models.

Intelligent Systems for Recognizing Hate Speech and Offensive Content: A multi-class classification model to detect hate speech, offensive language, and neutral content on social media.

Pre-trained-Language-Models-for-Abusive-and-Hate-speech-Classification-in-Arabic-Text: dziribert, arabet,..