sis 23(6):

Research Article

Artificial intelligence to reduce misleading publications on social networks

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  • @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
José Armando Tiznado Ubillús1,*, Marysela Ladera-Castañeda2,*, César Augusto Atoche Pacherres3,*, Miguel Ángel Atoche Pacherres4,*, Carmen Lucila Infante Saavedra3,*
  • 1: National University of San Marcos
  • 2: Federico Villarreal National University
  • 3: Universidad Nacional de Piura
  • 4: Universidad César Vallejo
*Contact email: jose.tiznado@unmsm.edu.pe, mladera@unfv.edu.pe, catochep@unp.edu.pe, matochepac@ucvvirtual.edu.pe, cinfantes@unp.edu.pe

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.