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Multilingual Offensive Lexicon consists of the first contextual lexicon for abusive language detection, which is composed of 1,000 explicit and implicit terms and expressions with any pejorative connotation annotated with contextual information
Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
An android application created under the framework of the Mandola EU project by the University of Cyprus. Allows the reporting of social media posts containing hate speech to relevant authorities.
This repository contains Korean Hate Speech dataset for paper, "K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment", accepted by COLING2022.
A project implementing better evaluation scenarios for community models for malicious content detection, and meta-learning GNNs to achieve better downstream adaptation.
Does fear speech exists in moderation free platform ? How does it compare with hate speech ? Our paper accepted in The PNAS (2022) tries to explore these questions in light of Gab Platform.
The task is a binary classification problem to classify the given dataset into two classes namely Hate Offensive tweets (HOF) and Non-Hate Offensive tweets (NOT). The task appeared as Subtask-A in HASOC 2021. The dataset taken is sampled from Twitter. It consists of twitter posts in Hindi and Hinglish language.
Empower online spaces with TextSecureAI – an innovative Cyberbullying Detection project. Utilizing advanced sentiment analysis, Naive Bayes, and SVM, this system ensures swift identification of hate speech on Twitter. Achieving 95% precision, it's a formidable guardian against online toxicity. Join us in creating a safer digital world! 🌐🛡️