Hateminers : Detecting Hate speech against Women
This addresses the problem of identifying misogynistic content for online moderation, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled detecting hate speech against women online by developing machine learning models for the Automatic Misogyny Identification shared task, achieving first place in English Subtask A and fifth in English Subtask B.
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.