Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach

Abstract

In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we propose a weakly supervised two-path bootstrapping approach for an online hate speech detection model leveraging large-scale unlabeled data. This system significantly outperforms hate speech detection systems that are trained in a supervised manner using manually annotated data. Applying this model on a large quantity of tweets collected before, after, and on election day reveals motivations and patterns of inflammatory language.

Published in IJCNLP 2017

References in corpus (2)

• Convolutional Neural Networks for Sentence Classification
• A C-LSTM Neural Network for Text Classification

Cited by in corpus (2)

• The Enemy Among Us: Detecting Hate Speech with Threats Based 'Othering' Language Embeddings
• Hierarchical CVAE for Fine-Grained Hate Speech Classification