Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks

Social media have increased the amount of information that people consume as well as the number of interactions between them. Nevertheless, most people tend to promote their favored narratives and hence form polarized groups. This encourages polarization and extremism resulting in extreme violence....

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Autores principales: Oppenheim, Abi, Albanese, Federico, Feuerstein, Esteban
Formato: Objeto de conferencia Resumen
Lenguaje:Inglés
Publicado: 2022
Materias:
Ciencias Informáticas
machine learning
social media
Reddit
data mining
toxicity
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/151588
https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/248/207
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-151588
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spelling I19-R120-10915-1515882023-05-03T19:59:46Z http://sedici.unlp.edu.ar/handle/10915/151588 https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/248/207 issn:2451-7496 Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks Oppenheim, Abi Albanese, Federico Feuerstein, Esteban 2022-10 2022 2023-04-17T18:54:22Z en Ciencias Informáticas machine learning social media Reddit data mining toxicity Social media have increased the amount of information that people consume as well as the number of interactions between them. Nevertheless, most people tend to promote their favored narratives and hence form polarized groups. This encourages polarization and extremism resulting in extreme violence. Against this backdrop, it is in our interest to find environments, strategies and mechanisms that allow us to reduce toxicity on social media (defining “toxicity” as a rude, disrespectful or unreasonable comment that is likely to make people leave a discussion). We address the hypothesis that a higher cultural diversity among community users reduces the toxicity of the user messages. We use Reddit as a case study, since this platform is characterized by a variety of discussion sub-forums where users debate political and cultural issues. Using community2vec, we generate an embedding for each community that allows us to portray users in a demographic and ideological aspect. In order to analyze each user statement, we process the data with different models, thereby obtaining which are the topics of debate and what are the levels of aggressiveness and negativism in them. Finally, we will seek to corroborate the hypothesis by analyzing the relationship between the cultural diversity present in each discussion group and the toxicity found in their posts. Sociedad Argentina de Informática e Investigación Operativa Objeto de conferencia Resumen http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 28-29
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
machine learning
social media
Reddit
data mining
toxicity
spellingShingle Ciencias Informáticas
machine learning
social media
Reddit
data mining
toxicity
Oppenheim, Abi
Albanese, Federico
Feuerstein, Esteban
Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
topic_facet Ciencias Informáticas
machine learning
social media
Reddit
data mining
toxicity
description Social media have increased the amount of information that people consume as well as the number of interactions between them. Nevertheless, most people tend to promote their favored narratives and hence form polarized groups. This encourages polarization and extremism resulting in extreme violence. Against this backdrop, it is in our interest to find environments, strategies and mechanisms that allow us to reduce toxicity on social media (defining “toxicity” as a rude, disrespectful or unreasonable comment that is likely to make people leave a discussion). We address the hypothesis that a higher cultural diversity among community users reduces the toxicity of the user messages. We use Reddit as a case study, since this platform is characterized by a variety of discussion sub-forums where users debate political and cultural issues. Using community2vec, we generate an embedding for each community that allows us to portray users in a demographic and ideological aspect. In order to analyze each user statement, we process the data with different models, thereby obtaining which are the topics of debate and what are the levels of aggressiveness and negativism in them. Finally, we will seek to corroborate the hypothesis by analyzing the relationship between the cultural diversity present in each discussion group and the toxicity found in their posts.
format Objeto de conferencia
Resumen
author Oppenheim, Abi
Albanese, Federico
Feuerstein, Esteban
author_facet Oppenheim, Abi
Albanese, Federico
Feuerstein, Esteban
author_sort Oppenheim, Abi
title Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
title_short Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
title_full Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
title_fullStr Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
title_full_unstemmed Toxicity, polarizations and cultural diversity in social networks : Using machine learning and natural language processing to analyze these phenomena in social networks
title_sort toxicity, polarizations and cultural diversity in social networks : using machine learning and natural language processing to analyze these phenomena in social networks
publishDate 2022
url http://sedici.unlp.edu.ar/handle/10915/151588
https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/248/207
work_keys_str_mv AT oppenheimabi toxicitypolarizationsandculturaldiversityinsocialnetworksusingmachinelearningandnaturallanguageprocessingtoanalyzethesephenomenainsocialnetworks
AT albanesefederico toxicitypolarizationsandculturaldiversityinsocialnetworksusingmachinelearningandnaturallanguageprocessingtoanalyzethesephenomenainsocialnetworks
AT feuersteinesteban toxicitypolarizationsandculturaldiversityinsocialnetworksusingmachinelearningandnaturallanguageprocessingtoanalyzethesephenomenainsocialnetworks
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