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arxiv: 2604.09635 · v1 · submitted 2026-03-20 · 💻 cs.CY · cs.AI· cs.LG

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

Jos\'e Manuel Alcalde-Llergo , Mariana Buenestado Fern\'andez , Carlos Enrique George-Reyes , Andrea Zingoni , Enrique Yeguas-Bol\'ivar This is my paper

Pith reviewed 2026-05-15 09:00 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.LG
keywords machine learningmedia literacydisinformationeducationprediction modelssurvey analysisclassification algorithms
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The pith

Machine learning models predict media and information literacy competencies against disinformation using student survey data.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops machine learning models to assess students' ability to handle disinformation through their media and information literacy skills. It collects survey responses from 723 students in education and communication programs and applies classification and regression algorithms to forecast competencies while identifying key factors. Results indicate that more complex models achieve higher accuracy than simpler ones. Academic year and prior training emerge as variables that notably boost prediction performance. Such models could support the creation of focused teaching programs to strengthen critical responses to misleading content in digital settings.

Core claim

Complex machine learning models outperform simpler approaches when predicting media and information literacy competencies in the context of disinformation, with academic year and prior training significantly improving prediction accuracy on data from 723 students.

What carries the argument

Classification and regression algorithms trained on validated survey responses to predict MIL competencies and isolate influencing variables such as academic year and prior training.

If this is right

  • Targeted educational interventions can be designed using the identified key variables to build disinformation resistance.
  • Personalized strategies can be created to improve students' critical navigation of digital environments.
  • Training programs for future educators and communicators can be adjusted based on predicted competency levels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested on working professionals or general adult populations to check if the same factors remain predictive.
  • Combining the models with ongoing digital behavior data might allow real-time adjustment of literacy support.
  • Curriculum planners could use the variable rankings to prioritize certain years or training modules in media education.

Load-bearing premise

The validated survey accurately measures actual media and information literacy competencies related to disinformation and the sample of 723 students generalizes to future educators and communicators.

What would settle it

A follow-up study on a new group of students in which simpler models achieve equal or higher accuracy than complex models would indicate the claimed superiority does not hold.

Figures

Figures reproduced from arXiv: 2604.09635 by Andrea Zingoni, Carlos Enrique George-Reyes, Enrique Yeguas-Bol\'ivar, Jos\'e Manuel Alcalde-Llergo, Mariana Buenestado Fern\'andez.

Figure 1
Figure 1. Figure 1: Most useful features selected by the different feature selection methods in order to predict the knowledge branch tests. The FFS identified a different subset, incorporating K3, A3, and R5, while also retaining the Disinfo Training condition in both states. Notably, the inclusion of A3 and R5 suggests that this method captures additional dependencies between features that may not be evident through individ… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper develops machine learning models to predict Media and Information Literacy (MIL) competencies in relation to disinformation using survey data from 723 students in education and communication programs. It applies classification and regression algorithms to these data, reports that complex models outperform simpler ones, and identifies academic year and prior training as key variables that improve prediction accuracy. The work aims to inform targeted educational interventions.

Significance. If the survey measures actual competencies with predictive validity and the models are shown to generalize via proper out-of-sample validation, the identification of modifiable factors such as prior training could support the design of more effective MIL curricula for future educators and communicators. The data-driven framing is a modest strength.

major comments (3)
  1. [Methods] Methods section: The abstract and text refer to a 'validated survey' but supply no information on content validity, reliability statistics (e.g., Cronbach's alpha), pilot testing, or—most critically—any behavioral validation linking self-reports to objective performance on disinformation-detection tasks such as source evaluation or manipulated-image identification. Because all ML targets derive from these labels, the reported model superiority is only as reliable as the label quality.
  2. [Results] Results section: The claim that 'complex models outperform simpler approaches' is presented without details on train/test splits, cross-validation scheme, performance metrics with uncertainty estimates, or confirmation that accuracy reflects out-of-sample rather than in-sample fit. This information is required to evaluate whether the performance gains are robust.
  3. [Discussion] Discussion section: The assumption that findings from the 723-student sample generalize to future educators and communicators is not supported by any analysis of sample representativeness or external validity checks.
minor comments (2)
  1. [Abstract] Abstract: The specific classification and regression algorithms employed are not named; listing them would improve reproducibility.
  2. [Discussion] The manuscript would benefit from a limitations subsection that explicitly addresses social-desirability bias in self-reported MIL data.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address each of the major comments below, indicating the revisions we plan to make to the manuscript.

read point-by-point responses

  1. Referee: [Methods] Methods section: The abstract and text refer to a 'validated survey' but supply no information on content validity, reliability statistics (e.g., Cronbach's alpha), pilot testing, or—most critically—any behavioral validation linking self-reports to objective performance on disinformation-detection tasks such as source evaluation or manipulated-image identification. Because all ML targets derive from these labels, the reported model superiority is only as reliable as the label quality.

    Authors: The survey instrument was adapted from previously validated MIL scales in the literature. However, we agree that the manuscript lacks explicit details on validation procedures. In the revised version, we will expand the Methods section to describe the survey development, including references to source instruments, pilot testing conducted, and reliability statistics such as Cronbach's alpha. We acknowledge that behavioral validation against objective tasks was not performed in this study; we will add this as a limitation in the Discussion and suggest it as an avenue for future research. revision: partial

  2. Referee: [Results] Results section: The claim that 'complex models outperform simpler approaches' is presented without details on train/test splits, cross-validation scheme, performance metrics with uncertainty estimates, or confirmation that accuracy reflects out-of-sample rather than in-sample fit. This information is required to evaluate whether the performance gains are robust.

    Authors: We appreciate this point and agree that methodological transparency is crucial. The revised manuscript will include a detailed account of the data splitting procedure (e.g., 70/30 train/test split with stratified sampling), the cross-validation approach (e.g., 5-fold CV), and report performance metrics including accuracy, F1-score, RMSE for regression, along with uncertainty estimates such as standard deviations across folds to confirm out-of-sample performance. revision: yes

  3. Referee: [Discussion] Discussion section: The assumption that findings from the 723-student sample generalize to future educators and communicators is not supported by any analysis of sample representativeness or external validity checks.

    Authors: The sample was drawn specifically from education and communication programs to target future educators and communicators. To address concerns about generalizability, the revised Discussion will include a subsection on sample characteristics, demographic comparisons to national statistics where available, and explicit discussion of limitations regarding external validity. We will also propose directions for multi-institutional studies to enhance generalizability. revision: partial

Circularity Check

0 steps flagged

No significant circularity: empirical ML fitting on survey data

full rationale

The paper collects survey responses from 723 students using a validated instrument, then applies standard classification and regression algorithms to predict MIL competency scores and identify influential variables such as academic year. No derivation step reduces a claimed prediction to its own inputs by construction, no self-citation chain supports a uniqueness claim, and no ansatz is smuggled in. Model performance comparisons are data-driven and self-contained against the collected dataset; any out-of-sample evaluation would be independent of the fitting process itself. Survey validity and behavioral grounding are separate methodological issues outside the circularity analysis.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unexamined assumption that the survey instrument validly captures MIL skills and that the student sample is representative; no free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption The validated survey accurately measures Media and Information Literacy competencies in the context of disinformation
    The study relies on this survey as the ground truth for training and evaluating the ML models.

pith-pipeline@v0.9.0 · 5481 in / 1128 out tokens · 37322 ms · 2026-05-15T09:00:39.149537+00:00 · methodology

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