The study was led by Porismita Borah, a professor in Washington State University’s Edward R. Murrow College of Communications and corresponding author of the new publication. Her co-authors were Ziyao Zhang, a PhD student at WSU; Xiaohui Cao, a PhD student at the University of Wisconsin-Madison; and Danielle Ka Lai Lee, an assistant professor at Hong Kong Shue Yan University. Their work addresses one of the most stubborn problems in the misinformation literature: corrections often fail, and even when they succeed, it has been unclear why. People who are told that a claim they believe or shared is wrong can feel challenged or insulted, react defensively, or simply doubt the credibility of whoever is doing the correcting.

Borah has spent roughly a decade studying how misinformation spreads and how it might be countered, and her latest publication advances the field’s understanding of how empathy functions in correction messages. Prior research in this area has produced conflicting conclusions, with some studies finding that a warm, understanding tone reduces misperceptions and others finding no such effect. Borah’s team added a crucial new layer to the question by testing the effectiveness of tone against the expectations and beliefs of the person receiving the correction, rather than treating all audiences as interchangeable.

The central concept in that layer is anthropomorphism, the well-documented psychological tendency to assign human characteristics to animals, machines, and other non-human entities. In the context of conversational AI, anthropomorphism describes the degree to which a user perceives an AI system as humanlike rather than as a purely technical tool. The study’s key insight is that the optimal communication strategy differs across this spectrum. For people who view AI strictly as a machine, corrections delivered in a neutral, just-the-facts tone proved most persuasive. For those who believe AI can be humanlike, an empathetic, understanding tone worked best.

What makes the finding striking is what did not matter. In multiple studies, the team found that corrections could work most of the time, but the source of the correction was largely irrelevant to its persuasive power. As Borah explained, it did not necessarily matter whether the correction came from a human being or an AI agent; what mattered was the tone and how that tone aligned with people’s beliefs about whether AI agents should behave more like humans or more like machines. In other words, a mismatch between message style and user expectations could blunt even an accurate correction delivered by a well-designed system.

To test these effects rigorously, the researchers conducted a randomized online experiment with 857 parents of children within the age range recommended to receive the vaccine for human papillomavirus, or HPV. The choice of topic was deliberate. HPV is spread through sexual contact and can cause a range of serious health problems, including several cancers. The HPV vaccine is considered safe and effective, yet it has been the subject of widespread and persistent misinformation, making it an ideal case for studying how false health beliefs form and how they might be corrected.

Participants were first evaluated for their level of anthropomorphism belief, then shown a simulated Facebook comment thread. The thread began with a false claim stating that HPV vaccines increase the risk of neurological problems. An AI corrections account then engaged with the claim directly in the comment thread, and the style of its response varied. The neutral answers used direct, plain language, along the lines of stating that the claim was not true and that scientific studies had shown no link between HPV vaccines and the neurological conditions in question. The empathetic tone was warmer, opening with an acknowledgment of the person’s concern, such as expressing that the researchers heard the individual, before pivoting to the scientific evidence.

The results were clear: the correction was most effective at reducing misperceptions when its tone matched the respondent’s anthropomorphism beliefs. A machinelike user confronted with a just-the-facts correction updated their beliefs; a user inclined to see AI as humanlike responded better to warmth and understanding. The finding adds an important dimension to the technical design of automated fact-checking systems, because it implies that a one-size-fits-all correction strategy will systematically fail for part of any large audience. Matching conversational style to individual expectations of the technology could unlock measurably better outcomes at population scale.

The practical implications reach well beyond academic journals. The authors suggest that social media platforms, government agencies, news organizations, and other institutions that confront health misinformation could target their AI fact-checking agents according to a user’s level of anthropomorphism. One proposed approach is a simple onboarding step for users of platforms or accounts that assesses their anthropomorphic tendencies, allowing the AI’s conversational tone to be adjusted accordingly. Such an intervention would be technically lightweight compared to the far harder task of changing underlying beliefs, and it could be integrated into existing comment-moderation and community-notes systems that already deploy AI to flag or respond to dubious claims.

As Borah notes, the problem of misinformation is critical and it is not going away, and the effectiveness of corrections depends on many factors, including the way accurate information is communicated; an empathetic tone may often work better than a condescending one, while factors such as race and gender also shape how messages land. Her conclusion is a sober reminder for engineers and communicators alike: researchers are ultimately trying to study humans, and humans are remarkably complex. The lesson of this study is not that AI is a silver bullet against falsehood, but that the oldest rule of persuasion, know your audience, applies with full force even when the messenger is a machine.

Subject of Research: How the tone of AI-generated corrections, matched to users' anthropomorphism beliefs, reduces vaccine misinformation

Article Title: AI can help correct medical misinformation — when it uses the right tone

Article References: AI can help correct medical misinformation — when it uses the right tone. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, misinformation, health communication, HPV vaccine, anthropomorphism, vaccine misinformation, social media, empathy, fact-checking, persuasion, human-computer interaction, Washington State University

The study, led by John Cook of the Melbourne Centre for Climate Change at the University of Melbourne together with Bärbel Winkler, Collin Maessen, Timo Lubitz, Doug Bostrom and Dana Nuccitelli, surveyed visitors who arrived at Skeptical Science rebuttals directly from Google searches. Between November 2021 and July 2025, the site displayed a pop-up invitation to 858,016 visitors. Of the 13,432 people who consented and completed an initial survey, 6,261 — about 46 percent — went on to read a rebuttal to the end and complete a follow-up question, providing the researchers with an unusually large and ecologically valid dataset.

The experimental design was elegantly simple. Each participant was shown a single statement about climate change — either a factual claim or a piece of misinformation, randomly assigned — and asked to rate their agreement on a six-point scale from strongly agree to strongly disagree. They then read the rebuttal they had clicked through to see. If they scrolled to the end, a second pop-up asked them to rate the same statement again. The researchers scored answers so that higher values always meant more accurate perceptions, allowing a direct before-and-after comparison of belief in both facts and myths.

The first striking finding concerned who visits the site at all. Nearly half of participants — 46.3 percent — already showed full agreement with climate facts or full disagreement with climate myths before reading anything, and 66.5 percent agreed with climate facts overall. The distribution of pre-existing beliefs was sharply bi-modal, with peaks at the extreme ends of the spectrum and only a minority of undecided visitors in the middle. This suggests the website serves two distinct audiences: people already convinced of climate science who are looking for ammunition to counter misinformation in their own conversations, and people who genuinely doubt the science but are open enough to search for answers.

The authors argue this pattern challenges a dismissive interpretation of such audiences. Critics have suggested that convinced visitors merely engage in what one researcher called chanting to the choir. But Cook and colleagues favour a more constructive framing: the site is teaching the choir to sing, equipping people with material to push back against misinformation. That matters because fear of confrontation drives what researchers call climate silence — many people avoid discussing climate change with friends and family altogether, producing a self-reinforcing spiral of silence. Conversely, discussing the issue raises awareness and triggers a positive feedback loop of further discussion.

The core result was encouraging but nuanced. Overall accuracy improved slightly after reading the rebuttals, though the combined change was not statistically significant. Looking at the two statement types separately revealed why: agreement with myth statements dropped significantly, which is exactly what debunking is supposed to achieve, but agreement with factual statements also dropped significantly — an unwelcome and counterproductive side effect. The two effects partially cancelled each other out. The researchers measured discernment, defined as the gap between belief in facts and belief in myths, and found it did increase overall, but the decline in fact acceptance remains a genuine concern for an organisation whose mission is to raise climate literacy.

Perhaps the most hopeful finding was who changed most. A strong negative correlation emerged between initial accuracy and the magnitude of belief change: the less accurate someone’s starting position, the greater their improvement after reading a rebuttal. Among participants who began with inaccurate views, 7.2 percent switched to accurate responses after a single reading. In other words, the rebuttals were most effective precisely on the people who needed them most — those most dismissive of climate science — suggesting that even brief, single-exposure corrections can reach the hardest-to-shift segment of the audience.

To understand why some rebuttals worked better than others, the team analysed the twenty rebuttals with the most data and then qualitatively examined the three best and three worst performers. The pattern was clear. Effective rebuttals, such as the one debunking the claim that climate impacts are not bad, communicated a clear replacement fact — that the harms of warming far outweigh the benefits — simply and repeatedly. Ineffective ones, such as the basic and intermediate versions of the claim that carbon dioxide was higher in the past, failed to state the key fact that a cooler ancient sun offset higher carbon dioxide levels, and failed to name the single-cause fallacy the myth commits. Psychological research recommends a fact-myth-fallacy-fact structure, and the analysis showed the strongest rebuttals hewed closest to it.

The findings arrive at a moment when the landscape of climate misinformation is shifting. Recent research shows contrarian claims increasingly target climate solutions rather than climate science itself — so-called discourses of delay — and solutions misinformation has proven especially polarising among political conservatives. Skeptical Science has responded by adding rebuttals of renewable energy myths, adapted from work with Columbia Law School’s Sabin Center, but the effectiveness of such corrections remains understudied. The team is now redesigning the website to integrate explicit fallacy explanations into its content infrastructure, hoping to lift fact acceptance while continuing to erode myth acceptance.

The study has limitations the authors acknowledge candidly. It measured only a single outcome — agreement with one statement — captured just one exposure to a correction whose effects are known to decay over time, and relied on a self-selected sample of website visitors rather than a representative population sample. Yet that same self-selection gave the field test real-world external validity that laboratory experiments cannot match. The researchers call for controlled experiments to test repeated exposure and isolate the contributions of replacement facts and fallacy explanations. For now, the message is clear: debunking works, especially on the most sceptical readers, but even the best corrections must be engineered with care, because a poorly constructed rebuttal can inadvertently erode belief in the very facts it sets out to defend.

Subject of Research: Effectiveness of online rebuttals in reducing climate change misperceptions among website visitors

Article Title: Quantifying the impact of Skeptical Science rebuttals in reducing climate misperceptions

Article References: Cook, J., Winkler, B., Maessen, C. J. H. M., Lubitz, T., Bostrom, D., & Nuccitelli, D. (2026). Quantifying the impact of Skeptical Science rebuttals in reducing climate misperceptions. Geoscience Communication, 9(2), 145-160. https://doi.org/10.5194/gc-9-145-2026

Image Credits: AI Generated

DOI: 10.5194/gc-9-145-2026

Keywords: climate misinformation, Skeptical Science, debunking, science communication, misinformation correction, climate myths, discernment, inoculation theory, public perceptions, Geoscience Communication, fact-checking, climate literacy

The acronym behind the project’s name refers to foreign information manipulation and interference, a term that has increasingly replaced the narrower notion of disinformation in European security discourse. FIMI encompasses coordinated efforts by external actors to distort the information environment, whether through fabricated narratives, manipulated media, coordinated inauthentic behavior, or targeted influence operations. Unlike simple falsehoods, these campaigns are often strategic, adaptive, and designed to exploit societal fault lines. Countering them requires more than fact-checking; it demands technical systems capable of detecting coordinated activity at scale, assessing the provenance and integrity of content, and supporting human analysts who must make rapid judgments under pressure. It is precisely this complex ecosystem of tools and practices that FIMI-RESIST intends to put to the test.

The timing of the initiative reflects a disinformation landscape that is changing faster than the defenses built to contain it. Generative artificial intelligence has dramatically lowered the barriers to producing synthetic text, images, audio, and video, allowing manipulated media to be created cheaply and scaled rapidly across platforms and languages. At the same time, the AI systems that people increasingly rely on to find and assess information, including search assistants and conversational agents, have themselves become targets for manipulation. An adversary who can poison the data or outputs of such systems can reach audiences indirectly, bypassing the traditional defenses of newsrooms and fact-checking organizations. These shifting threat vectors form the backdrop against which the project’s evaluation framework will be constructed.

What distinguishes FIMI-RESIST from earlier efforts is its deliberate refusal to build yet another stand-alone tool. The European Union has already funded a broad range of research projects and software solutions aimed at detecting and mitigating disinformation, but these have typically been developed in isolation, each with its own assumptions, datasets, and evaluation metrics. The result is a fragmented landscape in which it is nearly impossible to compare performance or determine which approaches are suited to which problems. The project addresses this gap by establishing a shared evaluation and harmonization framework: a common set of criteria and benchmarks against which twenty existing European tools will be assessed. The objective is to generate robust, comparable evidence about which methods perform best for specific users, operational settings, and threat scenarios.

The scientific foundation for this assessment draws on the coordinating group’s particular expertise. The XplaiNLP Research Group at Mainz combines natural language processing with explainable artificial intelligence and robust information processing, fields that are central to modern content analysis. Natural language processing provides the computational machinery for analyzing text at scale, identifying narrative patterns, and tracing how claims spread across networks. Explainable AI addresses a persistent weakness of automated systems: the tendency of machine learning models to function as opaque black boxes whose judgments cannot be easily scrutinized. In a domain where decisions can affect freedom of expression and media pluralism, the ability to explain why a system flagged a piece of content is not a luxury but a requirement. Robust information processing, meanwhile, concerns the resilience of these systems against adversarial attempts to deceive or evade them.

A central focus of the project is practical application rather than laboratory performance. Working with journalists, fact checkers, security stakeholders, and civil society organizations, the consortium will assess how effectively existing tools can be integrated into day-to-day workflows. This user-centered approach recognizes a common failure mode in security technology: a system that performs well on benchmark datasets may prove unusable in a busy newsroom or a crisis-response setting, where time constraints, language diversity, and the sheer volume of content create conditions that benchmarks rarely capture. By testing tools in realistic operational contexts, the project aims to identify not only which systems detect manipulation effectively, but also where gaps remain as AI-driven threats continue to evolve.

The consortium assembled for this task spans a deliberately wide range of competencies and geographies. Alongside the coordinating group at Johannes Gutenberg University Mainz, it includes the Kempelen Institute of Intelligent Technologies in Slovakia led by Dr. Jakub Šimko, the Centre for Research and Technology Hellas in Greece represented by Dr. Symeon Papadopoulos, and the University of Amsterdam in the Netherlands under Professor Richard Rogers. The technology and industry side is represented by Logically/INSIKT, operating in the United Kingdom and Spain, Factiverse from Norway, Exorde Labs from France, Cyfluence Research Center from Germany, and Gretchen AI, also from Germany, which participates as an associated partner. Media and civil society perspectives come from Deutsche Welle, Germany’s international broadcaster, and VoxUkraine, an organization with direct experience of operating in an information environment shaped by war and foreign interference.

The inclusion of Ukrainian and media partners underscores the stakes involved. Ukraine has become, in effect, a live testing ground for foreign information manipulation at scale, and organizations like VoxUkraine bring firsthand knowledge of what interference campaigns look like in practice. Deutsche Welle contributes decades of experience in international journalism and in monitoring disinformation across languages and regions. Civil society and security stakeholders round out the picture, ensuring that the evaluation framework reflects the needs of those who defend democratic discourse rather than purely academic metrics. The project has also drawn political support, with Alexandra Geese, a Member of the European Parliament, backing the initiative, and contributions at its kickoff from Saman Nazari of Alliance4Europe and Sandra Fiene of the European Commission, alongside policy and project officers Ilona von Bethlenfalvy and Alberto Domini.

Beyond its technical evaluations, FIMI-RESIST is designed to leave a durable institutional footprint. The project will establish a help desk, a European hub, and a program of training offerings to ensure that validated methods and results remain accessible to practitioners after the funding period ends. This sustainability component reflects a lesson learned from previous research projects, whose tools and findings often faded from view once grant support concluded. By building infrastructure for continued knowledge transfer, the consortium hopes to convert its thirty months of work into a lasting European capability rather than a one-time report.

The findings are also intended to feed directly into the European regulatory debate. The Digital Services Act, which imposes obligations on large online platforms to address systemic risks including disinformation; the AI Act, which establishes rules for artificial intelligence systems including transparency requirements; and the European Media Freedom Act, which protects pluralism and independence in the media landscape, together form a regulatory architecture whose effectiveness depends on an accurate understanding of which countermeasures succeed in practice. By supplying rigorous comparative evidence, FIMI-RESIST aims to inform how these frameworks are implemented and refined. In an era when the integrity of the information environment has become a matter of democratic security, knowing which defenses actually hold is perhaps the most valuable intelligence Europe can produce.

Subject of Research: Evaluation of European countermeasures against foreign information manipulation and interference

Article Title: Disinformation and influence operations: Which countermeasures actually work?

Article References: Disinformation and influence operations: Which countermeasures actually work?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: disinformation, FIMI, FIMI-RESIST, Horizon Europe, natural language processing, explainable AI, generative AI, fact-checking, Digital Services Act, media freedom, influence operations, Johannes Gutenberg University Mainz

The central insight behind DES-CMR is that earlier evidence-aware detection systems have had a structural blind spot. Graph-based methods, which represent claims and evidence documents as nodes in a network and preserve the structured relationships between them, have performed well in recent years. But in most of these systems, the step that filters candidate evidence is detached from the step that actually reasons over it. A model might first score documents for relevance, throw away the low scorers, and only then begin the real analytical work. That two-stage design throws away information: the filtering scores themselves encode a judgment about which documents matter, and once the cut has been made, that judgment can no longer be revised in light of what the surviving documents reveal about each other.

DES-CMR, whose name compresses the phrases differentiable evidence selection and credibility-aware multi-document reasoning, closes that gap with what the authors call selection-guided multi-document reasoning. The model begins by learning a claim-conditioned sparse evidence distribution, a mathematical object the paper denotes with the Greek letter omega. In plain terms, omega is a set of weights that says, for a given claim, how much each candidate evidence document should contribute. Crucially, this distribution is learned in a differentiable, end-to-end manner, meaning the entire system, from raw text to final verdict, is trained as one continuous pipeline rather than as loosely connected stages. The selection process is therefore not a heuristic bolted on the front of the model but an integral part of what the network optimizes.

What makes the approach distinctive is how that selection signal is reused. Rather than serving only as a preliminary filtering score, omega is injected as a prior into the attention mechanism that governs interactions between evidence documents. Attention, the workhorse of modern neural language models, normally decides how much each element in a sequence should influence every other element based on learned content similarity. By biasing that attention with the evidence-selection distribution, DES-CMR ensures that documents the model initially found promising get a stronger voice in shaping one another’s representations, while still allowing genuinely informative content to override the prior. The selection weights are then reused a second time during the final aggregation of evidence into a verdict, so the model’s initial judgment continues to shape the outcome even after deeper processing has occurred.

That deeper processing is where the multi-document reasoning happens. After the cross-document interaction layer refines the candidate representations, the model recomputes a distinct post-reasoning distribution, denoted pi. This second distribution allows the system to preserve or revise its initial evidence selection in light of three kinds of relationships that the paper highlights: complementary, redundant, and conflicting. Complementary documents each contribute unique facts that together build a case; redundant documents repeat one another and can be down-weighted without loss; and conflicting documents contradict each other, forcing the model to weigh credibility rather than simply average their signals. Having two distributions, one before and one after reasoning, gives the model a form of computational second thought, a mechanism that mirrors how a human fact-checker might revisit an initial shortlist of sources after reading them closely.

Text alone, however, is rarely enough to adjudicate conflicting evidence, and this is where the credibility-aware part of the model earns its name. DES-CMR supplements textual relevance with task-learned source embeddings, vector representations of the sources behind each evidence document that are learned during training rather than hand-coded from reputation lists. On top of these embeddings, the model applies a bounded source-conditioned logit residual, a small, capped adjustment to the final decision scores that reflects how reliable a source has proven to be. The bounding is a deliberate safety measure: it ensures that source reputation can nudge a verdict but cannot dominate the actual content of the evidence, protecting the system from both over-trusting authoritative outlets and unfairly dismissing unfamiliar ones.

Converting a probability into a hard label, the final step of any classifier, is a deceptively tricky decision, and the authors address it with a technique they call validation-aware decision-boundary selection. Instead of fixing the decision threshold at the conventional 0.5, the model selects the probability-to-label conversion boundary using validation data, stabilizing this conversion and, according to the paper’s analyses, improving the reliability of the final predictions. The representation and optimization backbone of the system rests on two further components: prompt-guided graph encoding, which structures the claim and its evidence into a graph the network can process, and adversarial supervised contrastive learning, a training strategy that pushes the model to learn representations robust to perturbations by simultaneously pulling similar examples together and pushing dissimilar ones apart under adversarial pressure.

The experimental case for DES-CMR rests on two widely used fact-checking datasets, Snopes and PolitiFact, which contain real-world claims paired with evidence documents and truth labels. Across these benchmarks, the model consistently outperformed representative baselines drawn from three families of approaches: pattern-based methods that rely on linguistic and stylistic cues, evidence-based methods that reason over retrieved documents, and graph-based methods that model claim-evidence structure. Beyond the headline comparisons, the study includes component ablations showing the contribution of each mechanism, robustness analyses probing behavior under perturbation, decision-boundary experiments validating the threshold-selection strategy, complexity analyses assessing computational cost, and sensitivity analyses examining how the system responds to its hyperparameters. Together, these evaluations aim to map both the effectiveness and the limitations of the proposed reasoning mechanism, an unusually thorough treatment for a single architecture paper.

The broader significance of this work lies in what it says about the future of automated fact-checking. As misinformation campaigns grow more sophisticated, detection systems increasingly need to do more than spot suspicious writing styles or match claims against a database; they need to reason the way an investigator does, gathering evidence, cross-referencing sources, noticing contradictions, and revising initial impressions. DES-CMR’s contribution is to show that this reasoning process can be made differentiable, meaning it can be trained end to end with gradient descent like any other neural network, and that credibility signals can be woven into the reasoning itself rather than applied as an afterthought. The authors have released the supporting data on GitHub, allowing other researchers to scrutinize and extend the approach. Whether selection-guided reasoning will scale to the sprawling, multimodal misinformation ecosystems of social media remains an open question, but the study offers a concrete, testable blueprint for evidence-aware AI that thinks before it judges, and that, in the fight against fake news, may prove to be exactly the kind of scrutiny the problem demands.

Subject of Research: Differentiable evidence selection and credibility-aware multi-document reasoning for fake news detection

Article Title: Differentiable evidence selection with credibility-aware multi-document reasoning for fake news detection

Article References: Sun, Y., & Wu, Y. (2026). Differentiable evidence selection with credibility-aware multi-document reasoning for fake news detection. Knowledge and Information Systems, 68(1), Article 270. https://doi.org/10.1007/s10115-026-02909-9

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02909-9

Keywords: fake news detection, evidence-aware verification, differentiable evidence selection, multi-document reasoning, graph neural networks, source credibility, attention mechanisms, contrastive learning, fact-checking, Snopes, PolitiFact, machine learning

The study, led by Ana Pérez-Escoda of Universidad Francisco de Vitoria in Madrid, together with Eglée Ortega-Fernández of the Complutense University of Madrid and Laura Martínez-Otón of Universidad Nebrija, forms part of a European Union-funded Erasmus Plus project called WISE-ME. The project’s premise is simple but urgent: disinformation spreads fastest on social networks, and that is precisely where teachers need to be equipped to intervene. Nearly two decades have passed since digital competence was written into European education legislation, yet the researchers argue that the rapid evolution of social media among minors and the explosion of fake news demand a continuous renewal of what teachers know and how they teach it.

To map the landscape, the team surveyed 1,186 students aged 12 to 18 and 166 secondary school teachers in Spain, Austria, Greece, the Czech Republic, Bulgaria and Denmark. Because the data did not follow a normal distribution, the researchers turned to non-parametric statistics, applying the Mann–Whitney U test to compare the two independent groups at a significance threshold of p < 0.05. The questionnaire itself was a substantial instrument: 35 variables and 150 items organized into four blocks covering sociodemographics, social media use, information management and disinformation management. A panel of experts from all six countries refined the items, and the instrument achieved a Cronbach’s alpha of 0.847, comfortably above the 0.70 threshold conventionally accepted as evidence of high internal reliability.

The theoretical backbone of the work is DigCompEdu, the European Framework for the Digital Competence of Educators, which has been adopted by every EU member state. The framework organizes teacher digital competence into six domains and 22 sub-competences, and the study concentrated on three of them: supporting teaching and learning, empowering learners, and facilitating learners’ digital competence. It is this last domain that places responsibility on teachers to develop students’ skills in media literacy, communication and digital citizenship, content creation, responsible technology use, and problem solving. In Spain, the framework has been adapted into a national reference model, and recent curriculum reforms under Royal Decree 217/2022 explicitly require students to identify disinformation and assess the reliability of sources.

The statistical comparison exposed deep generational divides. On trustworthiness, the differences were significant for every major platform tested: Facebook, Instagram, TikTok, X, WhatsApp, YouTube, Be Real, Discord, Pinterest and Snapchat all showed p-values below 0.05. Students placed notably more trust in the platforms they used most heavily, particularly Instagram, TikTok and WhatsApp, a finding the researchers describe as a departure from earlier studies in which young people tended to be skeptical of the very services they frequented. Both groups, however, reported obtaining most of their information from the internet and social networks, confirming that the news habits of adults and adolescents have converged even as their critical habits have not.

When it came to recognizing fake news, teachers expressed far more confidence: six out of ten said they often or always recognize disinformation, compared with only four out of ten students. Sharing behavior told a more troubling story. While 64.5 percent of teachers said they had never shared fake news, only 52.3 percent of students could say the same. More than 32 percent of students admitted to sharing false content unknowingly, versus 20 percent of teachers, and 15.4 percent of students acknowledged having shared fake news at some point, more than double the teacher figure of 7.2 percent. Perceptions of why fake news is created also split along generational lines: teachers were significantly more likely to attribute it to malicious intent, political interest and economic gain, while students and teachers agreed on boredom and fun as motivations, a divergence the authors link to adolescents’ still-developing ideological awareness.

Perhaps the most revealing results concerned how the two groups judge information. Students were significantly more likely than teachers to assess content based on the number of likes it received or whether an influencer had shared it, while teachers leaned on the credibility of the source. Both groups, however, were influenced by messages received privately from friends, a pattern the researchers interpret through Social Influence Theory and the filter bubble phenomenon, in which social environments shape beliefs and reinforce existing views. Verification habits were weak across the board: only about 18 percent of students said they fact-check information often or always, compared with nearly 38 percent of teachers, and while most students in both groups reported using some strategy or tool to confirm what reaches them, systematic fact-checking remains far from routine.

These findings became the blueprint for a set of training modules designed in the study’s second phase. Module 1 gives students a framework for understanding disinformation and information disorders, identifying the sources and motivations behind them and evaluating credibility. Module 2 addresses information consumption patterns, and Module 3 tackles the psychological dimension, training students to identify the cognitive biases that color their perception of social media content and to recognize common manipulative techniques. Module 4 promotes responsible digital citizenship and respectful, informed online behavior. When teachers reported difficulty fitting the materials into their formal curricula, the team added a sixth, methodological module to help educators adapt the content to their own subjects, including guidance on building a classroom Personal Learning Environment.

The researchers are candid about the study’s limitations. The convenience sample was designed to recruit committed school communities rather than to be nationally representative, the cross-sectional design precludes causal inference, and no factor analysis was performed to confirm the dimensional structure of the 150-item questionnaire. The training modules themselves have not yet been implemented, so their effectiveness remains untested, though an interactive bot to support teachers and students is planned as a next step. Even so, the team argues the work represents a meaningful advance by confronting two challenges that curricula formally acknowledge but classrooms often fail to deliver: the pervasive use of social media by minors and the spread of disinformation.

The broader message is one of structural urgency. The authors contend that the digital divide between teachers and students is not merely a matter of literacy but a generational and methodological gap that demands rethinking initial and ongoing teacher education, revising school curricula, and building training programs that go beyond instrumental technical skills toward security, content creation and critical engagement. As long as young people navigate digital spaces with ease but without pedagogical support, the researchers warn, the online environment remains a risky one, and the paradox of trusting the platforms that spread the lies will persist. Equipping teachers to turn social media from a threat into a teaching tool, they conclude, is a social responsibility that requires sustained and unwavering commitment.

Subject of Research: Teacher digital competence and media literacy for combating disinformation and fake news on social media in secondary education

Article Title: Enhancing teachers’ digital competence for combating disinformation and fake news through the use of social media in classroom

Article References: Pérez-Escoda, A., Ortega-Fernández, E., & Martínez-Otón, L. (2026). Enhancing teachers’ digital competence for combating disinformation and fake news through the use of social media in classroom. Journal of New Approaches in Educational Research, 15(1), Article 4. https://doi.org/10.1007/s44322-026-00051-6

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00051-6

Keywords: digital competence, teachers, disinformation, fake news, social media, media literacy, secondary education, DigCompEdu, fact-checking, teacher training, digital divide, critical thinking