The Social Media Hate Speech Barometer: Making of

[EN] Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily...

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Detalles Bibliográficos
Autores: Sauer, Sebastian, Piazza, Alexander, Schacht, Sigurd
Tipo de recurso: capítulo de libro
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/206335
Acceso en línea:https://riunet.upv.es/handle/10251/206335
Access Level:acceso abierto
Palabra clave:Hate speech
Machine learning
Cybersecurity
Natural language processing
Artificial intelligence
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spelling The Social Media Hate Speech Barometer: Making ofSauer, SebastianPiazza, AlexanderSchacht, SigurdHate speechMachine learningCybersecurityNatural language processingArtificial intelligence[EN] Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-03-12book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/206335reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2063352026-06-13T07:49:27Z
dc.title.none.fl_str_mv The Social Media Hate Speech Barometer: Making of
title The Social Media Hate Speech Barometer: Making of
spellingShingle The Social Media Hate Speech Barometer: Making of
Sauer, Sebastian
Hate speech
Machine learning
Cybersecurity
Natural language processing
Artificial intelligence
title_short The Social Media Hate Speech Barometer: Making of
title_full The Social Media Hate Speech Barometer: Making of
title_fullStr The Social Media Hate Speech Barometer: Making of
title_full_unstemmed The Social Media Hate Speech Barometer: Making of
title_sort The Social Media Hate Speech Barometer: Making of
dc.creator.none.fl_str_mv Sauer, Sebastian
Piazza, Alexander
Schacht, Sigurd
author Sauer, Sebastian
author_facet Sauer, Sebastian
Piazza, Alexander
Schacht, Sigurd
author_role author
author2 Piazza, Alexander
Schacht, Sigurd
author2_role author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Hate speech
Machine learning
Cybersecurity
Natural language processing
Artificial intelligence
topic Hate speech
Machine learning
Cybersecurity
Natural language processing
Artificial intelligence
description [EN] Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-03-12
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/206335
url https://riunet.upv.es/handle/10251/206335
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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