Detection of Propaganda in Information Warfare using Deep Learning

Pandey, Rashmikiran; Pandey, Mrinal; Nazarov, A. S.

doi:10.1109/icac3n56670.2022.10074449

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Cited by 3 publications

References 23 publications

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Pandey,

Nazarov

2023

Int. j. inf. tecnol.

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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Pandey,

Nazarov

2023

Int. j. inf. tecnol.

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Forecasting Election Sentiments: Deep Learning vs. Traditional Models

Bhadauria,

Shukla,

Kumar

et al. 2024

2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

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Mathematical and Computer Modeling of a Dynamic System for Effectively Combating Disinformation

Kereselidze

2024

Wseas Transactions on Systems

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The work investigated a mathematical and computer model of a dynamic system for effectively combating disinformation. In the compartmental model of false information dissemination in society, there are groups of citizens: - At risk, prone to the perception of misinformation; Adepts - those who accepted false information and with Immunity - those who rejected false information from the very beginning or future adepts. A barrier level for the number of adherents will be introduced as a measure of the information security of society. As a result of a computer experiment, the possibility of an uncontrolled growth in the number of adherents was identified, threatening the safety of society as a whole.

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Detection of Propaganda in Information Warfare using Deep Learning

Cited by 3 publications

References 23 publications

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

Forecasting Election Sentiments: Deep Learning vs. Traditional Models

Mathematical and Computer Modeling of a Dynamic System for Effectively Combating Disinformation

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