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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2022: Advances in Knowledge Discovery and Data Mining pp 93–105Cite as

Coded Hate Speech Detection via Contextual Information

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 13280)

Abstract

Hate speech on online social media seriously affects the experience of common users. Many online social media platforms deploy automatic hate speech detection programs to filter out hateful content. To evade detection, coded words have been used to represent the targeted groups in hate speech. For example, on Twitter, “Google” is used to indicate African-Americans, and “Skittles” is used to indicate Muslim. As a result, it would be difficult to determine whether a hateful text including “Google” targets African-Americans or the search engine. In this paper, we develop a coded hate speech detection framework, called CODE, to detect hate speech by judging whether coded words like Google or Skittles are used in the coded meaning or not. Based on a proposed two-layer structure, CODE is able to detect the hateful texts with observed coded words as well as newly emerged coded words. Experimental results on a Twitter dataset show the effectiveness of our approach.

Keywords

  • Coded hate speech
  • Few-shot learning

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  • DOI: 10.1007/978-3-031-05933-9_8
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Fig. 1.
Fig. 2.

Notes

  1. 1.

    https://dictionary.cambridge.org/dictionary/english/hate-speech.

  2. 2.

    https://knowyourmeme.com/memes/events/operation-google.

  3. 3.

    https://github.com/allenai/allennlp/blob/v0.9.0/tutorials/how_to/elmo.md.

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Acknowledgement

This work was supported in part by NSF grants 1564250, 1946391, and 2103829.

Author information

Authors and Affiliations

  1. University of Arkansas, Fayetteville, AR, 72701, USA

    Depeng Xu, Angela Uchechukwu Nwude, Lu Zhang, Anna Zajicek & Xintao Wu

  2. Utah State University, Logan, UT, 84322, USA

    Shuhan Yuan

  3. Paycom Software, Inc., Oklahoma City, OK, 73142, USA

    Yueyang Wang

Authors
  1. Depeng Xu
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  2. Shuhan Yuan
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  3. Yueyang Wang
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  4. Angela Uchechukwu Nwude
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  5. Lu Zhang
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  6. Anna Zajicek
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  7. Xintao Wu
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Corresponding author

Correspondence to Shuhan Yuan .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Prof. Dr. Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Assoc. Prof. Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Xu, D. et al. (2022). Coded Hate Speech Detection via Contextual Information. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-05933-9_8

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