# Hateminers : Detecting Hate speech against Women

arXiv: https://arxiv.org/abs/1812.06700 · HTML: https://mlsanity.com/paper/1812.06700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2018.

## Built on by (most cited first, 5 of 5)

- [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021)
- [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://mlsanity.com/paper/2104.01443/) (2021)
- [Hypothesis Engineering for Zero-Shot Hate Speech Detection](https://mlsanity.com/paper/2210.00910/) (2022)
- [Hate versus Politics: Detection of Hate against Policy makers in Italian tweets](https://mlsanity.com/paper/2107.05357/) (2021)
- [Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference](https://mlsanity.com/paper/2204.07010/) (2022)

## Abstract (arXiv)

With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content. In this paper, We present the machine learning models developed for the Automatic Misogyny Identification (AMI) shared task at EVALITA 2018. We generate three types of features: Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet. These features are then concatenated and fed into the machine learning models. Our model came First for the English Subtask A and Fifth for the English Subtask B. We release our winning model for public use and it's available at https://github.com/punyajoy/Hateminers-EVALITA.