Artificial intelligence to reduce misleading publications on social networks

Ubillús J.A.T.; Ladera-Castañeda M.; Pacherres C.A.A.; Pacherres M.Á.A.; Saavedra C.L.I. · EAI Endorsed Transactions on Scalable Information Systems (2023) · DOI: 10.4108/eetsis.3894

Resumen

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.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional de Piura; Universidad Nacional Federico Villarreal; Universidad César Vallejo