Automatic social media news classification: a topic modeling approach

 

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Detalles Bibliográficos
Autores: Amador, Daniel, Gamboa-Venegas, Carlos, García, Ernesto, Segura-Castillo, Andrés
Formato: artículo original
Estado:Versión publicada
Fecha de Publicación:2022
Descripción:Social media has modified the way that people access news and debate about public issues. Although access to a myriad of data sources can be considered an advantage, some new challenges have emerged, as issues about content legitimacy and veracity start to prevail among users. That transformation of the public sphere propels problematic situations, such as misinformation and fake news. To understand what type of information is being published, it is possible to categorize news automatically using computational tools. Thereby, this short paper presents a platform to retrieve and analyze news, along with promising results towards automatic news classification using a topic modeling approach, which should help audiences to identify news content easier and discusses possible routes to improve the situation in the near future.
País:RepositorioTEC
Institución:Instituto Tecnológico de Costa Rica
Repositorio:RepositorioTEC
Lenguaje:Inglés
Español
OAI Identifier:oai:repositoriotec.tec.ac.cr:2238/14160
Acceso en línea:https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6477
https://hdl.handle.net/2238/14160
Access Level:acceso abierto
Palabra clave:Automatic news classification
social media
topic modeling
Clasificación automática de noticias
Modelado de tópicos
Redes sociales