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Volume 16
Issue 11
10.3390/info16110929
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation

by
José Manuel Alcalde-Llergo
1,2,
Mariana Buenestado Fernández
3,
Carlos Enrique George-Reyes
4,
Andrea Zingoni
2 and
Enrique Yeguas-Bolívar
1,*
1
Department of Computer Science, University of Córdoba, 14071 Córdoba, Spain
2
Department of Economics, Engineering, Society and Business Organization, University of Tuscia, 01100 Viterbo, Italy
3
Department of Education, University of Córdoba, 14071 Córdoba, Spain
4
Dirección de Investigación, Vicerrectoría de Investigación y Vinculación, Universidad Bolivariana del Ecuador, Durán 170507, Ecuador
*
Author to whom correspondence should be addressed.
Information 2025, 16(11), 929; https://doi.org/10.3390/info16110929 (registering DOI)
Submission received: 24 September 2025 / Revised: 17 October 2025 / Accepted: 21 October 2025 / Published: 23 October 2025
Versions Notes

Abstract

This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among students, particularly future educators and communicators. While the digital revolution has expanded access to information, it has also amplified the spread of false and misleading content, making MIL essential for fostering critical thinking and responsible media engagement. Despite its relevance, predictive modeling of MIL in relation to disinformation remains underexplored. To address this gap, a quantitative study was conducted with 723 students in education and communication programs using a validated survey. Classification and regression algorithms were applied to predict MIL competencies and identify key influencing factors. Results show that complex models outperform simpler approaches, with variables such as academic year and prior training significantly improving prediction accuracy. These findings can inform the design of targeted educational interventions and personalized strategies to enhance students’ ability to critically navigate and respond to disinformation in digital environments.
Keywords: media and information literacy; disinformation; higher education; machine learning media and information literacy; disinformation; higher education; machine learning

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MDPI and ACS Style

Alcalde-Llergo, J.M.; Fernández, M.B.; George-Reyes, C.E.; Zingoni, A.; Yeguas-Bolívar, E. Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation. Information 2025, 16, 929. https://doi.org/10.3390/info16110929

AMA Style

Alcalde-Llergo JM, Fernández MB, George-Reyes CE, Zingoni A, Yeguas-Bolívar E. Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation. Information. 2025; 16(11):929. https://doi.org/10.3390/info16110929

Chicago/Turabian Style

Alcalde-Llergo, José Manuel, Mariana Buenestado Fernández, Carlos Enrique George-Reyes, Andrea Zingoni, and Enrique Yeguas-Bolívar. 2025. "Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation" Information 16, no. 11: 929. https://doi.org/10.3390/info16110929

APA Style

Alcalde-Llergo, J. M., Fernández, M. B., George-Reyes, C. E., Zingoni, A., & Yeguas-Bolívar, E. (2025). Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation. Information, 16(11), 929. https://doi.org/10.3390/info16110929

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