:bookmark_tabs: Galician corpus for misogyny detection

SemEval 2022 Task 5: Multimedia Automatic Misogyny Identification - baseline models and dataset

MEME benchmark dataset for misogyny detection

Multimodal Misogyny Detection - SemEval 2022 - MAMI Challenge

Temis is an Automatic Misogyny Identification tool. Using Deep Learning models, it can be used to predict whether a text contains misogyny, which type it is and to whom it is targetted.

DetectHer is a lightweight ML project that detects gendered harassment on Twitter using interpretable models like Logistic Regression and SVM. Built from scratch with real-time scraped data and TF-IDF features, it balances accuracy, speed, and transparency, no black-box models, no GPU needed.

This repository contains the code for submission made at SemEval 2022 Task 5: MAMI

Hateful Meme challenge, Knowledge graph based approach

This repository hosts a high-performance deep learning model designed to identify misogynistic content in Arabic social media text. Developed as an academic project, the model successfully overcomes significant linguistic challenges, such as dialectal variation and high Out-of-Vocabulary (OOV) rates.

Ben-Misog: A Benchmark Dataset for Misogynistic Comments in Bengali and Its Baseline Evaluation

Bengali Misogyny Identification with Deep Learning and LIME.