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2020 | OriginalPaper | Chapter

Not All Swear Words Are Used Equal: Attention over Word n-grams for Abusive Language Identification

Authors : Horacio Jesús Jarquín-Vásquez, Manuel Montes-y-Gómez, Luis Villaseñor-Pineda

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

The increasing propagation of abusive language in social media is a major concern for supplier companies and governments because of its negative social impact. A large number of methods have been developed for its automatic identification, ranging from dictionary-based methods to sophisticated deep learning approaches. A common problem in all these methods is to distinguish the offensive use of swear words from their everyday and humorous usage. To tackle this particular issue we propose an attention-based neural network architecture that captures the word n-grams importance according to their context. The obtained results in four standard collections from Twitter and Facebook are encouraging, they outperform the \(F_1\) scores from state-of-the-art methods and allow identifying a set of inherently offensive swear words, and others in which its interpretation depends on its context.

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Footnotes
1
Taken from the Offensive Language Identification Dataset [18].
 
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Metadata
Title
Not All Swear Words Are Used Equal: Attention over Word n-grams for Abusive Language Identification
Authors
Horacio Jesús Jarquín-Vásquez
Manuel Montes-y-Gómez
Luis Villaseñor-Pineda
Copyright Year
2020
Book
Pattern Recognition

Print ISBN: 978-3-030-49075-1

Electronic ISBN: 978-3-030-49076-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-49076-8_27

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