short-paper

Hate speech detection using brazilian imageboards

Authors:

Gabriel Nascimento,

Flavio Carvalho,

Alexandre Martins da Cunha,

Carlos Roberto Viana,

Gustavo Paiva GuedesAuthors Info & Claims

WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web

Pages 325 - 328

https://doi.org/10.1145/3323503.3360619

Published: 29 October 2019 Publication History

Abstract

With the changes in human interaction prompted by the development of communications platforms over the internet, hate speech and offensive language emerged as a contemporary problem. Social networks allow users with different opinions and backgrounds to interact without direct eye-to-eye contact. It brings a sense of safety to promote hate speech, which is even more significant in anonymous environments. There are sites called imageboards, composed of different boards aggregating different topics. On some boards, anonymous users widely promote hate speech. However, only a few works in literature have focused on hate speech in imageboards content. This work aims to classify Brazilian Portuguese texts to detect hate speech, using data from the Brazilian 55chan imageboard to build a dataset with hate speech content. Three classifiers were trained to hate speech binary classification. The Linear Support Vector Classifier achieved the best result with 0.955 of F1-score.

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Cited By

Stecuła KPradela J(2024)Virtual Reality in language education in the context of technology developmentLanguage, Technology, Humanities in Society 5.010.14220/9783737017633.203(203-218)Online publication date: 17-Nov-2024
https://doi.org/10.14220/9783737017633.203
Kumar RRatan SSingh SNandi EDevi LBhagat ADawer YLahiri BBansal A(2024)A multilingual, multimodal dataset of aggression and bias: the ComMA datasetLanguage Resources and Evaluation10.1007/s10579-023-09696-758:2(757-837)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10579-023-09696-7
Weitzel LDaroz TCunha LHelde RMorais L(2023)Investigating Deep Learning Approaches for Hate Speech Detection in Social Media : Portuguese-BR tweets2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211751(1-5)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10211751
Show More Cited By

Index Terms

Hate speech detection using brazilian imageboards
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social media

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cover image ACM Other conferences

WebMedia '19: Proceedings of the 25th Brazillian Symposium on Multimedia and the Web

October 2019

537 pages

ISBN:9781450367639

DOI:10.1145/3323503

General Chairs:
Joel dos Santos
CEFET/RJ
,
Débora Christina Muchaluat Saade
UFF
,
Maria da Graça C. Pimentel
University of Sao Paulo, Brazil
,
Alessandra Alaniz Macedo
University of Sao Paulo, Brazil

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2019

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Conselho Nacional de Desenvolvimento Científico e Tecnológico

Conference

WebMedia '19

WebMedia '19: Brazilian Symposium on Multimedia and the Web

October 29 - November 1, 2019

Rio de Janeiro, Brazil

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Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Stecuła KPradela J(2024)Virtual Reality in language education in the context of technology developmentLanguage, Technology, Humanities in Society 5.010.14220/9783737017633.203(203-218)Online publication date: 17-Nov-2024
https://doi.org/10.14220/9783737017633.203
Kumar RRatan SSingh SNandi EDevi LBhagat ADawer YLahiri BBansal A(2024)A multilingual, multimodal dataset of aggression and bias: the ComMA datasetLanguage Resources and Evaluation10.1007/s10579-023-09696-758:2(757-837)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10579-023-09696-7
Weitzel LDaroz TCunha LHelde RMorais L(2023)Investigating Deep Learning Approaches for Hate Speech Detection in Social Media : Portuguese-BR tweets2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211751(1-5)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10211751
Steen EYurechko KKlug D(2023)You Can (Not) Say What You Want: Using Algospeak to Contest and Evade Algorithmic Content Moderation on TikTokSocial Media + Society10.1177/205630512311945869:3Online publication date: 31-Aug-2023
https://doi.org/10.1177/20563051231194586
Ollagnier ACabrio EVillata S(2023)Unsupervised fine-grained hate speech target community detection and characterisation on social mediaSocial Network Analysis and Mining10.1007/s13278-023-01061-413:1Online publication date: 29-Mar-2023
https://doi.org/10.1007/s13278-023-01061-4
Trajano DBordini RVieira R(2023)OLID-BR: offensive language identification dataset for Brazilian PortugueseLanguage Resources and Evaluation10.1007/s10579-023-09657-058:4(1263-1289)Online publication date: 3-May-2023
https://doi.org/10.1007/s10579-023-09657-0
Chhabra AVishwakarma D(2023)A literature survey on multimodal and multilingual automatic hate speech identificationMultimedia Systems10.1007/s00530-023-01051-829:3(1203-1230)Online publication date: 20-Jan-2023
https://doi.org/10.1007/s00530-023-01051-8
Sender GCarvalho FGuedes G(2021)The Happy Level: A New Approach to Measure Happiness at Work Using Mixed MethodsInternational Journal of Qualitative Methods10.1177/1609406921100241320Online publication date: 8-Apr-2021
https://doi.org/10.1177/16094069211002413
Pamungkas EBasile VPatti V(2021)Towards multidomain and multilingual abusive language detection: a surveyPersonal and Ubiquitous Computing10.1007/s00779-021-01609-127:1(17-43)Online publication date: 11-Aug-2021
https://doi.org/10.1007/s00779-021-01609-1
Poletto FBasile VSanguinetti MBosco CPatti V(2020)Resources and benchmark corpora for hate speech detection: a systematic reviewLanguage Resources and Evaluation10.1007/s10579-020-09502-8Online publication date: 30-Sep-2020
https://doi.org/10.1007/s10579-020-09502-8

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