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2019 | OriginalPaper | Chapter

Prediction Uncertainty Estimation for Hate Speech Classification

Authors : Kristian Miok, Dong Nguyen-Doan, Blaž Škrlj, Daniela Zaharie, Marko Robnik-Šikonja

Published in: Statistical Language and Speech Processing

Publisher: Springer International Publishing

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Abstract

As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. However, automatic approaches shall not jeopardize free speech, so they shall accompany their decisions with explanations and assessment of uncertainty. Thus, there is a need for predictive machine learning models that not only detect hate speech but also help users understand when texts cross the line and become unacceptable.
The reliability of predictions is usually not addressed in text classification. We fill this gap by proposing the adaptation of deep neural networks that can efficiently estimate prediction uncertainty. To reliably detect hate speech, we use Monte Carlo dropout regularization, which mimics Bayesian inference within neural networks. We evaluate our approach using different text embedding methods. We visualize the reliability of results with a novel technique that aids in understanding the classification reliability and errors.

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Metadata
Title
Prediction Uncertainty Estimation for Hate Speech Classification
Authors
Kristian Miok
Dong Nguyen-Doan
Blaž Škrlj
Daniela Zaharie
Marko Robnik-Šikonja
Copyright Year
2019
Book
Statistical Language and Speech Processing

Print ISBN: 978-3-030-31371-5

Electronic ISBN: 978-3-030-31372-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-31372-2_24

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