Detecting Online Hate Speech Using Context Aware Models

2017/10/20 by Lei Gao, Gao, Lei, Ruihong Huang +1 · 4 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Internet Traffic Analysis and Secure E-voting #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.1710.07395

openalex publication_date 2017/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Coefficient of Agreement for Nominal Scales
• Ex Machina: Personal Attacks Seen at Scale

Cited by

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• Web(er) of Hate: A Survey on How Hate Speech Is Typed
• Systematic Attack Surface Reduction For Deployed Sentiment Analysis\n Models
• On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance

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