An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets

Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours. With the rise of social networks like Twitter, hate speeches were swift beca...

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Autores principales: Gabriel Ichcanziho Pérez-Landa, Octavio Loyola-González, Miguel Angel Medina-Pérez
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Lenguaje:EN
Publicado: MDPI AG 2021
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Acceso en línea:https://doaj.org/article/13839795090f49bbaacc9732ee67102a
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spelling oai:doaj.org-article:13839795090f49bbaacc9732ee67102a2021-11-25T16:38:25ZAn Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets10.3390/app1122108012076-3417https://doaj.org/article/13839795090f49bbaacc9732ee67102a2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10801https://doaj.org/toc/2076-3417Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours. With the rise of social networks like Twitter, hate speeches were swift because of the pseudo feeling of anonymity that these platforms provide. Sometimes this violent behavior on social networks that begins as threats or insults to third parties breaks the Internet barriers to become an act of real physical violence. Hence, this proposal aims to correctly classify xenophobic posts on social networks, specifically on Twitter. In addition, we collected a xenophobic tweets database from which we also extracted new features by using a Natural Language Processing (NLP) approach. Then, we provide an Explainable Artificial Intelligence (XAI) model, allowing us to understand better why a post is considered xenophobic. Consequently, we provide a set of contrast patterns describing xenophobic tweets, which could help decision-makers prevent acts of violence caused by xenophobic posts on Twitter. Finally, our interpretable results based on our new feature representation approach jointly with a contrast pattern-based classifier obtain similar classification results than other feature representations jointly with prominent machine learning classifiers, which are not easy to understand by an expert in the application area.Gabriel Ichcanziho Pérez-LandaOctavio Loyola-GonzálezMiguel Angel Medina-PérezMDPI AGarticleXenophobiaTwitterExplainable Artificial IntelligenceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10801, p 10801 (2021)
institution DOAJ
collection DOAJ
language EN
topic Xenophobia
Twitter
Explainable Artificial Intelligence
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
spellingShingle Xenophobia
Twitter
Explainable Artificial Intelligence
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Gabriel Ichcanziho Pérez-Landa
Octavio Loyola-González
Miguel Angel Medina-Pérez
An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
description Xenophobia is a social and political behavior that has been present in our societies since the beginning of humanity. The feeling of hatred, fear, or resentment is present before people from different communities from ours. With the rise of social networks like Twitter, hate speeches were swift because of the pseudo feeling of anonymity that these platforms provide. Sometimes this violent behavior on social networks that begins as threats or insults to third parties breaks the Internet barriers to become an act of real physical violence. Hence, this proposal aims to correctly classify xenophobic posts on social networks, specifically on Twitter. In addition, we collected a xenophobic tweets database from which we also extracted new features by using a Natural Language Processing (NLP) approach. Then, we provide an Explainable Artificial Intelligence (XAI) model, allowing us to understand better why a post is considered xenophobic. Consequently, we provide a set of contrast patterns describing xenophobic tweets, which could help decision-makers prevent acts of violence caused by xenophobic posts on Twitter. Finally, our interpretable results based on our new feature representation approach jointly with a contrast pattern-based classifier obtain similar classification results than other feature representations jointly with prominent machine learning classifiers, which are not easy to understand by an expert in the application area.
format article
author Gabriel Ichcanziho Pérez-Landa
Octavio Loyola-González
Miguel Angel Medina-Pérez
author_facet Gabriel Ichcanziho Pérez-Landa
Octavio Loyola-González
Miguel Angel Medina-Pérez
author_sort Gabriel Ichcanziho Pérez-Landa
title An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
title_short An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
title_full An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
title_fullStr An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
title_full_unstemmed An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
title_sort explainable artificial intelligence model for detecting xenophobic tweets
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/13839795090f49bbaacc9732ee67102a
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