Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation
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arXiv
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| Format: | Preprint |
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2026
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| author | Alcalde-Llergo, José Manuel Fernández, Mariana Buenestado George-Reyes, Carlos Enrique Zingoni, Andrea Yeguas-Bolívar, Enrique |
| author_facet | Alcalde-Llergo, José Manuel Fernández, Mariana Buenestado George-Reyes, Carlos Enrique Zingoni, Andrea Yeguas-Bolívar, Enrique |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation Alcalde-Llergo, José Manuel Fernández, Mariana Buenestado George-Reyes, Carlos Enrique Zingoni, Andrea Yeguas-Bolívar, Enrique Computers and Society Artificial Intelligence Machine Learning 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. |
| title | Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.09635 |