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dc.date.accessioned 2023-04-17T18:54:22Z
dc.date.available 2023-04-17T18:54:22Z
dc.date.issued 2022
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/151588
dc.description.abstract 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. en
dc.format.extent 28-29 es
dc.language en es
dc.subject machine learning es
dc.subject social media es
dc.subject Reddit es
dc.subject data mining es
dc.subject toxicity es
dc.title Toxicity, polarizations and cultural diversity in social networks en
dc.type Objeto de conferencia es
sedici.identifier.uri https://publicaciones.sadio.org.ar/index.php/JAIIO/article/download/248/207 es
sedici.identifier.issn 2451-7496 es
sedici.title.subtitle Using machine learning and natural language processing to analyze these phenomena in social networks en
sedici.creator.person Oppenheim, Abi es
sedici.creator.person Albanese, Federico es
sedici.creator.person Feuerstein, Esteban es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Resumen es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
sedici.date.exposure 2022-10
sedici.relation.event Simposio Argentino de Ciencia de Datos y GRANdes DAtos (AGRANDA 2022) - JAIIO 51 (Modalidad virtual y presencial (UAI), octubre 2022) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)