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Detecting hate speech on the world wide web

Authors:

William Warner,

Julia HirschbergAuthors Info & Claims

LSM '12: Proceedings of the Second Workshop on Language in Social Media

Pages 19 - 26

Published: 07 June 2012 Publication History

Abstract

We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, religion, gender, or sexual orientation. While hate speech against any group may exhibit some common characteristics, we have observed that hatred against each different group is typically characterized by the use of a small set of high frequency stereotypical words; however, such words may be used in either a positive or a negative sense, making our task similar to that of words sense disambiguation. In this paper we describe our definition of hate speech, the collection and annotation of our hate speech corpus, and a mechanism for detecting some commonly used methods of evading common "dirty word" filters. We describe pilot classification experiments in which we classify anti-semitic speech reaching an accuracy 94%, precision of 68% and recall at 60%, for an F1 measure of. 6375.

References

[1]

{Choi et al 2005} Yejin Choi, Claire Cardie, Ellen Riloff, Siddharth Patwardhan, Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns. In HLT '05 Association for Computational Linguistics Stroudsburg, PA, USA, pp. 355--362, 2005

Digital Library

[2]

{Yarowsky 1994} David Yarowsky, Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French. In ACL-94, Stroudsburg, PA, pp. 88--95, 1994

Digital Library

[3]

{Yarowsky 1995} David Yarowsky, Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In ACL-95, Cambridge, MA, pp. 189--196, 1995.

Digital Library

[4]

{Nockleby 2000} John T. Nockleby, Hate Speech. In Encyclopedia of the American Constitution (2nd ed., edited by Leonard W. Levy, Kenneth L. Karst et al., New York: Macmillan, 2000), pp. 1277--1279 (see http://www.jiffynotes.com/a_study_guides/book_notes/eamc_03/eamc_03_01193.html)

[5]

{Stephens-Davidowitz 2011} Seth Stephens-Davidowitz, The Effects of Racial Animus on Voting: Evidence Using Google Search Data http://www.people.fas.harvard.edu/~sstephen/papers/RacialAnimusAndVotingSethStephensDavidowitz.pdf

[6]

{McDonald et al 2007} McDonald, R. Hannan, K. Neylon, T. Wells, M. Reynar, J. Structured Models for Fine-to-Coarse Sentiment Analysis. In ANNUAL MEETING- ASSOCIATION FOR COMPUTATIONAL LINGUISTICS 2007, CONF 45; VOL 1, pages 432--439

[7]

{Pang and Lee 2008} Pang, Bo and Lee, Lillian, Opinion Mining and Sentiment Analysis. In Foundations and Trends in Information Retrieval, issue 1--2, vol. 2, Now Publishers Inc., Hanover, MA, USA, 2008 pp. 1--135

Digital Library

[8]

{Pang, Lee and Vaithyanathan 2002} Pang, Bo and Lee, Lillian and Vaithyanathan, Shivakumar Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, Association for Computational Linguistics, Stroudsburg, PA, USA, 2002 pp. 79--86

Digital Library

[9]

{Qiu et al 2009} Qiu, Guang and Liu, Bing and Bu, Jiajun and Chen, Chun Expanding domain sentiment lexicon through double propagation. In Proceedings of the 21st international jont conference on Artificial intelligence, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 2009 pp. 1199--1204

Digital Library

[10]

{Joachims 1999} Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Schlkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999.

Digital Library

[11]

{Koo, Carreras and Collins 2008} Simple Semi-supervised Dependency Parsing In Proc. ACL/HLT 2008

[12]

{Xu and Zhu 2010} Filtering Offensive Language in Online Communities using Grammatical Relations

[13]

{A Razavi, Diana Inkpen, Sasha Uritsky, Stan Matwin 2010} Offensive Language Detection Using Multi-level Classification In Advances in Artificial Intelligence Springer, 2010, pp. 1627

Digital Library

[14]

{Attenberg and Provost 2010} Why Label When You Can Search?: Alternatives to active learning for applying human resources to build classification models under extreme class imbalance, KDD 2010

Digital Library

[15]

{Lipscombe, Venditti and Hirschberg 2003} Classifying Subject Ratings of Emotional Speech Using Acoustic Features. In Proceedings of Eurospeech 2003, Geneva.

Cited By

Zhao DAndrews JPapakyriakopoulos OXiang ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694580(60644-60673)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694580
Wang HYang TNaseem ULee RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and BilibiliProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681521(7493-7502)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681521
Duong PNguyen TNguyen H(2024)Fusion Network for Multimodal Hate Speech DetectionProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654562(1-1)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654562
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Detecting hate speech on the world wide web
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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cover image DL Hosted proceedings

LSM '12: Proceedings of the Second Workshop on Language in Social Media

June 2012

88 pages

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Association for Computational Linguistics

United States

Publication History

Published: 07 June 2012

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Cited By

Zhao DAndrews JPapakyriakopoulos OXiang ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)PositionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694580(60644-60673)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694580
Wang HYang TNaseem ULee RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MultiHateClip: A Multilingual Benchmark Dataset for Hateful Video Detection on YouTube and BilibiliProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681521(7493-7502)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681521
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https://dl.acm.org/doi/10.1145/3654522.3654562
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Thebault-Spieker JVenkatagiri SMine NLuther K(2023)Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content ModerationProceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency10.1145/3593013.3594080(1280-1291)Online publication date: 12-Jun-2023
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Kulkarni AMasud SGoyal VChakraborty TSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Revisiting Hate Speech Benchmarks: From Data Curation to System DeploymentProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599896(4333-4345)Online publication date: 6-Aug-2023
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