Code for analyzing hate speech tweets using Wikipedia-based contextual representations.
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Updated
Sep 10, 2020 - Python
Code for analyzing hate speech tweets using Wikipedia-based contextual representations.
Bachelor's thesis on removing hate from online comments using paraphrasing: algorithm DPhate
Example dataset and prompt design of Korean Offensive language Machine Generation (K-OMG), published at IJCNLP-AACL 2023.
Models to detect hateful comments served with flask trained on Kaggle's Toxic Comment Classification Challenge dataset.
PHARM (Preventing Hate Against Refugees and Migrants) is a European project funded by the European Union, within Rights, Equality and Citizenship program. The main goal of the project is to monitor and model hate speech against refugees and migrants in Greece, Italy and Spain in order to predict and combat hate crime.
Course project: Information Retrieval and Web Search
Baseline for the HatePic dataset
This repository contains data and code used in the paper "A Crosslingual Analysis of Homotransphobia on Twitter" (C3NLP Workshop @ EACL23).
Detecting hate speeches using ML by creating a pipeline model to get the highest accuracy.
Counter speech classification using adversarial training
Research project that detects hate speech in tweets using an attentive LSTM model.
Hate Speech classification in Italian using XLM (fine-tuning). Published at the WOAH workshop (NAACL2022).
Assignments for the Course Social Computing (CS60017) [IITKGP]
Joint work as part of a bachelor's thesis on utilizing a combination of NLP and CV methods in implementing multimodal approaches to combat hate speech in memes.
This work focuses on the development of machine learning models, in particular neural networks and SVM, where they can detect toxicity in comments. The topics we will be dealing with: a) Cost-sensitive learning, b) Class imbalance
Assisting newspaper moderators with machine learning.
An hate-speech recognizer implemented using deep learning methods. Pre-trained, fine-tuned and fully trained models were used. Approx. 90% accuracy rate was obtained w/ fine tuned and fully trained models.
Understanding hateful subreddits through text clustering
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