This position paper critiques the dominance of detection-centered approaches in misinformation research. We argue that the prevailing paradigm treats information disorders as a content-level anomaly to be identified and suppressed, thereby obscuring the structural conditions under which different forms of information disorders emerge and resonate. Drawing on critical anthropology, we propose an alternative “clinical” model: information disorders should be understood not only as informational distortion, but as a syndrome with complex causes embedded in contexts of economic precarity, institutional distrust, and informational inequality. Treating detection as the ends rather than the means of intervention risks misguiding our efforts. Rather than positioning NLP primarily as a tool for boundary enforcement, we outline a reorientation toward structural diagnosis: diversifying data beyond WEIRD contexts, extracting socioeconomic and trust-related signals from discourse, and integrating computational outputs within interdisciplinary causal frameworks. Under this model, detection becomes a means for an epidemiology of discourse, subordinated to the broader objective of cultivating long-term epistemic resilience in our online environments.

In the era of digital communication, the rapid spread of information presents significant challenges to society. This paper provides an in-depth examination of the existing frameworks developed to understand and address these phenomena. More precisely, this paper categorizes and compares various frameworks, including typology-based, process-oriented, impact-oriented, and actor-centric approaches. It highlights the strengths and limitations of each framework type, with a particular focus on their applicability to combat false information in diverse contexts. The paper underscores the importance of adopting a holistic and flexible approach that integrates multiple frameworks and adapts to the evolving nature of technology, particularly AI-driven false and misleading content.

State-of-the-art fake-news classifiers frequently report near-ceiling accuracy on widely used benchmarks such as ISOT, Misinfo, and WELFake. We argue that such results often reflect structural homogeneity and provenance-based separability rather than robust claim-level veracity inference. Anchored in the Information Disorder framework, we analyze how dataset construction operationalizes the notion of “fake” and how this shapes model behavior. We conduct systematic bidirectional cross-dataset experiments across six transfer directions and evaluate performance not only by mean accuracy, but also by variance and directional asymmetry. Results reveal substantial degradation under distribution shift and pronounced transfer asymmetries between dataset pairs. Although not always achieving the highest mean accuracy, affective augmentation combining dimensional (VAD) and categorical (Ekman) representations yields the lowest variance and smallest directional gap, indicating superior cross-domain stability. Our findings expose the disconnect between accuracy-driven benchmarking and construct-valid evaluation. We argue that progress in fake-news detection requires shifting from isolated in-domain optimization toward robustness-oriented, bidirectional, and distribution-aware assessment practices.

The proliferation of large language models (LLMs) has significantly increased the potential for automated dissemination of disinformation, necessitating robust systems for check-worthiness detection. However, existing models are primarily trained on human claims, leaving their performance on machine-generated text largely unexplored. In this paper, we benchmark encoder models (BERT and RoBERTa) and industry accessible tools (ClaimBuster) against LLM-paraphrased claims across three stylistic categories: syntactic restructuring, syntactic complexity and lexical informality. Our results indicate a consistent performance degradation on synthetic claims, particularly on complex and informal claims. We demonstrate that adversarial training significantly improves model resilience, with RoBERTa achieving F1-score gains up to +5.22 on the CheckIt dataset. Finally, SHAP analysis reveals that while base models rely on narrow syntactic heuristics such as active voice, robust models learn to anchor their prediction on core factual entities. These findings highlight the necessity of stylistic-aware training to maintain fact-checking efficacy in an increasingly LLM-populated information landscape.

Information disorder research overwhelmingly focuses on fabricated or manipulated content (fake news, deepfakes, propaganda) while comparatively neglecting the most pervasive form of distorted information: media bias. Unlike outright falsehoods, media bias operates within the boundaries of factual reporting, distorting public understanding through framing, omission, and word choice rather than fabrication. This makes it harder to detect, harder to regulate, and paradoxically more influential, since it originates from trusted mainstream sources rather than marginal actors. In this position paper, we argue that media bias should be recognized as a first-class category within information disorder frameworks. Drawing on the Wardle and Derakhshan (2017) taxonomy, communication theory, and a systematic review of over 100 studies on automated media bias detection, we demonstrate that current frameworks inadequately account for the systematic distortion of true content. We present a consolidated taxonomy of media bias types organized by linguistic level, compare detection paradigms across the information disorder and media bias communities, and identify four properties that make media bias uniquely dangerous: its scale, its source credibility, the invisibility of omission, and its cumulative normative effect. We conclude with an integrated research agenda grounded in specific gaps identified through the review.

Among news disorders, propagandist news are particularly insidious, because they tend to mix oriented messages with factual reports intended to look like reliable news. To detect propaganda, extant approaches based on Language Models such as BERT are promising but often overfit their training datasets, due to biases in data collection. To enhance classification robustness and improve generalization to new sources, we propose a neurosymbolic approach combining non-contextual text embeddings (fastText) with symbolic conceptual features such as genre, topic, and persuasion techniques. Results show improvements over equivalent text-only methods, and ablation studies as well as explainability analyses confirm the benefits of the added features. Keywords: Information disorder, Fake news, Propaganda, Classification, Topic modeling, Hybrid method, Neurosymbolic model, Ablation, Robustness

This position paper proposes a theory grounded NLP framework for information disorder detection integrating three explicitly connected dimensions: epistemic status, intentionality, and contextual harm. Moving beyond binary fake news classification, we argue that reliable intervention requires structured differentiation between verification outcomes, manipulation indicators, and consequence assessment. We provide concrete annotation schemas with decision rules for ambiguous cases, formal aggregation operators with monotonicity and escalation guarantees, explicit conflict resolution strategies for inconsistent signals, and standardized risk profile templates that translate multidimensional outputs into actionable routing policies. Synthesizing work on harm taxonomies, uncertainty quantification, and automated fact checking pipelines, we introduce an integration layer that preserves interpretability while enabling policy aligned deployment. We further propose a reformed evaluation protocol incorporating conformal prediction for principled abstention, calibration analysis, disagreement modeling, harm weighted metrics, and human uplift assessment to measure real decision support utility rather than standalone classifier accuracy. We position this framework as a conceptual and operational roadmap for structured misinformation assessment, outlining phased validation pathways while acknowledging that empirical validation remains essential future work.

Online misinformation research in NLP has expanded rapidly, including approaches that model affective signals such as sentiment, discrete emotions, and emotion dynamics. However, the Information Disorder framework distinguishes misinformation, disinformation, and malinformation along dimensions of intention, harm, and contextual dependence, which are rarely operationalised in current datasets, tasks, and evaluation protocols. We provide a systematic mapping of 82 studies at the intersection of Information Disorder and emotion-aware NLP (51 model papers, 7 dataset papers, 24 survey/theory papers). Across empirical works (58), veracity-centric supervision dominates (72.4% binary labels), while explicit intention and harm variables appear in only 1.7% each. Evaluation relies mostly on random splits (79.3%), limiting robustness to source and temporal shifts. Emotion is represented in 43.1% of model papers, mostly as static features, with emotion dynamics and audience emotion rare. Based on these findings, we propose an operational taxonomy aligned with Information Disorder and a benchmark blueprint specifying tasks, annotation variables, split strategies, and evaluation protocols to support theory-grounded, comparable progress.

The rise of Large Language Models has shifted the Information Disorder landscape toward automated threats. This study investigates the linguistic construction of synthetic news by comparing GPT-5, Gemini 2.5, and Grok 4 across English, Spanish, and Bulgarian. Using multilingual human-authored verified news and disinformation as seeds, we analyze how prompt informativeness and model architecture influence deceptive content production. Our methodology employs five metrics: semantic similarity, factual consistency, readability, lexical richness, and persuasion technique frequency. Our analysis reveals that while prompt scarcity leads to informational loss, LLMs maintain a homogenized stylistic template regardless of input length. Unlike human authors, who intensify rhetorical and emotional markers to drive deceptive intent, LLMs adhere to a neutral register. This study identifies distinct statistical patterns in generated content characterized by hyper-standardized readability and high lexical density (p < 0.001). These features serve as robust “LLM signatures”, enabling a classification accuracy of 96% across English, Spanish, and Bulgarian. These findings suggest that generated disinformation relies on invariant syntactic structures rather than nuanced human rhetoric, providing a framework for detection tools centered on structural patterns rather than content veracity.

This paper aims to contribute to the understanding of information disorder from an epistemological perspective, by analysing the internal/cognitive as well as the external/contextual factors that determine knowledge defects. In this regard, I first compare the concept of information with the properly epistemological concept of knowledge by arguing that the latter makes explicit the two fundamental prescriptive characteristics that beliefs should have—truth and justification—that remain implicit in the epistemically neutral notion of information. Therefore, I provide an externalist account of knowledge according to which a belief is true and justified to the extent that it allows for satisfactory adaptation to the environment, in which the ecological, technological and sociological environment itself becomes an integral part of an extended cognitive system. Based on this epistemological exploration of the notion of information through an externalist conception of knowledge, I suggest that disinformation can be understood as the state of an “ignorant” collective cognitive system, that is, a closed system that establishes interactions only within a virtual environment devoid of any semantic relevance and appeal to rational justification. In conclusion, I point out that, although the digital revolution appears to fuel the expansion of ignorance, it nevertheless poses challenges that allow epistemological reflection itself to renew addressing the crisis of knowledge with extended theoretical resources.

Disinformation has become a serious threat to the democratic stability of Western societies, with various conspiracy theories spreading from fringe spaces to mainstream media and politics. While some of these theories may seem merely absurd and harmless, others pose significant risks. Among the most dangerous are Population Replacement Conspiracy Theories (PRCTs), which promote the false narrative of a deliberate demographic substitution through immigration. Despite their disinformative nature, increasing widespread and documented connections to extremist violence and political polarization, current computational detection models primarily target COVID-19 or general conspiracy theories, lacking specialized annotated corpora and approaches for identifying PRCTs in multilingual contexts. In this work, we present the first systematic benchmark for PRCT detection in Portuguese and Italian.

Understanding how online narratives travel through coalitions is critical for identifying information disorder, yet computational analyses often rely on conservative network constructions that erase initially sparse but salient signals. This paper proposes a novel multi-layer framework that captures low-frequency signals of emerging information disorder allowing for locating where online discourse is reframed and amplified over time. The use case is 14 years of Italian discourse on X regarding the Human Papillomavirus (HPV) vaccine across three pivotal epochs (2010–2024). Utilizing hashtag co-occurrence networks, we introduce a dual-layer approach. We first identify robust core discourse coalitions through conservative community detection, revealing a stable prevention-oriented backbone contrasted with increasingly separable skepticism coalitions. We then introduce a ‘coverage’ layer and project fringe hashtags into core coalitions based on weighted connectivity. Using a manually labelled set of skeptical and conspiratorial seed tweets, we demonstrate that this core–coverage projection significantly improves the recovery of long-tail, problematic hashtags while preserving an interpretable coalition structure. Our findings characterize the structural maturation of polarized narratives and provide a methodology for mapping how discourse is reframed and amplified by information disorder over time.