Research Article
Artificial intelligence to reduce misleading publications on social networks
@ARTICLE{10.4108/eetsis.3894, author={Jos\^{e} Armando Tiznado Ubill\^{u}s and Marysela Ladera-Casta\`{o}eda and C\^{e}sar Augusto Atoche Pacherres and Miguel \^{A}ngel Atoche Pacherres and Carmen Lucila Infante Saavedra}, title={Artificial intelligence to reduce misleading publications on social networks}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={10}, number={6}, publisher={EAI}, journal_a={SIS}, year={2023}, month={10}, keywords={Disinformation, artificial intelligence, fake news, social media}, doi={10.4108/eetsis.3894} }- José Armando Tiznado Ubillús
Marysela Ladera-Castañeda
César Augusto Atoche Pacherres
Miguel Ángel Atoche Pacherres
Carmen Lucila Infante Saavedra
Year: 2023
Artificial intelligence to reduce misleading publications on social networks
SIS
EAI
DOI: 10.4108/eetsis.3894
Abstract
In this paper we investigated about the potential problems occurring worldwide, regarding social networks with misleading advertisements where some authors applied some artificial intelligence techniques such as: Neural networks as mentioned by Guo, Z., et. al, (2021), sentiment analysis, Paschen (2020), Machine learning, Burkov (2019) cited in Kaufman (2020) and, to combat fake news in front of such publications by social networks in this study were able to identify if these techniques allow to solve the fear that people feel of being victims of misleading news or fake videos without checking concerning covid-19. In conclusion, it was possible to detail in this paper that the techniques applied with artificial intelligence used did not manage to identify misleading news in a deep way. These techniques used are not real-time applications, since each artificial intelligence technique is separately, extracting data from the information of social networks, generating diagnoses without real-time alerts.
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