Detecting Online Hate Speech Using Context Aware Models

Abstract

In the wake of a polarizing election, the cyber world is laden with hate speech. Context accompanying a hate speech text is useful for identifying hate speech, which however has been largely overlooked in existing datasets and hate speech detection models. In this paper, we provide an annotated corpus of hate speech with context information well kept. Then we propose two types of hate speech detection models that incorporate context information, a logistic regression model with context features and a neural network model with learning components for context. Our evaluation shows that both models outperform a strong baseline by around 3% to 4% in F1 score and combining these two models further improve the performance by another 7% in F1 score.

Published in RANLP 2017

References in corpus (2)

• Ex Machina: Personal Attacks Seen at Scale
• Prediction of Cyberbullying Incidents on the Instagram Social Network

Cited by in corpus (6)

• Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
• Hate begets Hate: A Temporal Study of Hate Speech
• Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
• How Hateful are Movies? A Study and Prediction on Movie Subtitles
• An Information Retrieval Approach to Building Datasets for Hate Speech Detection
• Implicit Context-aware Learning and Discovery for Streaming Data Analytics