Study of different machine learning approaches for identification of fake news articles
In this document multiple machine learning approaches, including Supervised, Semi-supervised and Unsupervised learning are explored with the objective of finding the best algorithm for the task of identifying fake news. The corpus used consists on pure text data extracted from news articles. TF-IDF...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/184331 |
| Acceso en línea: | https://hdl.handle.net/2117/184331 |
| Access Level: | acceso abierto |
| Palabra clave: | Fake news Machine learning Aprenentatge automàtic Aprenentatge automàtic -- Algorismes Àrees temàtiques de la UPC::Informàtica |
| Sumario: | In this document multiple machine learning approaches, including Supervised, Semi-supervised and Unsupervised learning are explored with the objective of finding the best algorithm for the task of identifying fake news. The corpus used consists on pure text data extracted from news articles. TF-IDF and word2vec features are studied. Python is used for the implementation |
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