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

José Manuel Alcalde-Llergo; Mariana Buenestado Fernández; Carlos Enrique George-Reyes; Andrea Zingoni; Enrique Yeguas-Bolívar

cs.CYMar 20, 2026

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

Authors: José Manuel Alcalde-Llergo, Mariana Buenestado Fernández, Carlos Enrique George-Reyes, Andrea Zingoni, Enrique Yeguas-Bolívar

Organizations: Dept. of Computer Science, University of Córdoba, Córdoba, Spain · Dept. of Economics, Engineering, Society and Business Organization, University of Tuscia, Viterbo, Italy · Dept. of Education, University of Córdoba, Córdoba, Spain · Universidad Politécnica Metropolitana de Hidalgo, México

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.

Figures & tables

Id	Item	Factor 1	Factor 2	Factor 3
K1	I know what misinformation means.	0.706
K2	I can identify the characteristics of
	misinformation in a fake headline.	0.757
K3	I differentiate the concept of misinformation
	from similar terms such as ”fake news”,
	”rumors”, and ”false news”.	0.665

Table 1: Factor Analysis (Rotated Component Matrix)

Model		Train (CV)				Test
		Mean	Std	Mean	Std
Algorithm	Best Parameters	F1	F1	Acc	Acc	F1	Acc
	{’C’: 1, ’gamma’: ’scale’,
SVM	’kernel’: ’rbf’}	0.734	0.032	0.782	0.033	0.815	0.814
	{’criterion’: ’gini’,
	’max_depth’: 5,

Table 2: Results of the different models in predicting the knowledge branch, including the best configuration for each of the models.

Selector		Train (CV)				Test
Method	Best Features	Mean F1	Std F1	Mean Acc	Std Acc	F1	Acc
SKB	K1, K2, K3,
	Disinfo Training = False,
	Disinfo Training = True	0.682	0.030	0.763	0.019	0.751	0.752
FFS	K3, A3, R5,
	Disinfo Training = False,

Table 3: Results obtained for different feature selection methods

Figure 1: Most useful features selected by the different feature selection methods in order to predict the knowledge branch

Regression Target and Selected Model		Train (CV)		Test
Objective Variable	Best Model	Mean RMSE	Std MSE	Test RMSE
Knowledge	LR {’fit_intercept’: True}	2.201	0.311	2.287
	RF {’criterion’: ’squared_error’,
	’max_depth’: 5,
	’min_samples_split’: 20,
Skills	’n_estimators’: 100}	1.759	0.455	1.835

Table 4: Regression performance metrics for the evaluated models.

MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection
Mar 15, 2026Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos +4Fake News Detection Intent Classification
University of Padua, Italy · Polish-Japanese Academy of Information Technology, Poland · NASK National Research Institute, Poland +1
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May 24, 2026Soheila Gheisari, Hamid SalarianAI in Education Educational Data Mining
Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia
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Jun 29, 2026Sebastian Kula, Martin TamajkaMulti-Agent LLM Systems Fake News Detection
Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · West Pomeranian University of Technology in Szczecin, Szczecin, Poland

Mar 15, 2026J/K move · Enter open · S save

MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection

Arkadiusz Modzelewski, Witold Sosnowski, Eleni Papadopulos, Elisa Sartori+3

University of Padua, Italy · Polish-Japanese Academy of Information Technology, Poland · NASK National Research Institute, Poland+1

Fake News Detection Intent Classification LLM Reasoning Automated Fact-Checking

Open paper AskPDF arXiv

The intentional creation and spread of disinformation poses a significant threat to public discourse. However, existing English datasets and research rarely address the intentionality behind the disinformation. This work presents MALINT, the first human-annotated English corpus developed in collaboration with expert fact-checkers to capture disinformation and its malicious intent. We utilize our novel corpus to benchmark 12 language models, including small language models (SLMs) such as BERT and large language models (LLMs) like Llama 3.3, on binary and multilabel intent classification tasks. Moreover, inspired by inoculation theory from psychology and communication studies, we investigate whether incorporating knowledge of malicious intent can improve disinformation detection. To this end, we propose intent-based inoculation, an intent-augmented reasoning for LLMs that integrates intent analysis to mitigate the persuasive impact of disinformation. Analysis on six disinformation datasets, five LLMs, and seven languages shows that intent-augmented reasoning improves zero-shot disinformation detection. To support research in intent-aware disinformation detection, we release the MALINT dataset with annotations from each annotation step.

Abstract

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