# Navigating misinformation and disinformation: how definition ambiguity limits the DSA's implementation

Marion Seigneurin, Christine Balagué, Inna Lyubareva

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Marion Seigneurin, Christine Balagué, Inna Lyubareva. Navigating misinformation and disinformation: how definition ambiguity limits the DSA's implementation. European Journal of Communication, 2025, 40 (6), pp.619-646. 10.1177/02673231251391980. hal-05395628

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# Misinformation and Disinformation: Definition Ambiguity and the Limits of the DSA's Implementation

Marion Seigneurin (corresponding author)

Paris-Saclay University, Univ Evry, IMT-BS, LITEM, France
+33667647197

marion.seigneurin@imt-bs.eu

Christine Balagué

Institut Mines-Télécom Business School, LITEM, France

christine.balague@imt-bs.eu

Inna Lyubareva

IMT-Atlantique, LEGO, 29280, Brest, France

inna.lyubareva@imt-atlantique.fr

## Funding statement

This work was supported by the Future & Ruptures program, jointly funded by Institut Mines-Télécom (IMT), Fondation Mines-Télécom, Institut Carnot TSN, and Institut Carnot M.I.N.E.S. The Good in Tech research network, under the aegis of the Risk Foundation, provided additional financial support.

To cite this version please use:

Marion Seigneurin, Christine Balagué, Inna Lyubareva. (2025). Navigating Misinformation and Disinformation: How Definition Ambiguity Limits the DSA's Implementation. *European Journal of Communication*. DOI: 10.1177/02673231251391980
Abstract

Very large online platforms shape public discourse, raising concerns about the impact of misinformation and disinformation on democratic stability. The European Union responded with the Digital Services Act framework. However, its enforcement remains limited, partly due to conceptual ambiguities surrounding the concepts of misinformation and disinformation. This article investigates how definitional inconsistencies undermine the DSA's implementation. We analyzed 79 documents—including EU legislation, platform policies, fact-checking codes, and academic publications—to examine how key actors define these terms. We identified four key definitional criteria: content quality, intent, associated risk, and creation and dissemination techniques. Our findings reveal that these criteria reflect divergent institutional interests and result in fragmented definitions. This fragmentation generates critical risks, affecting freedom of expression, research, countermeasure design, intervention effectiveness, and impact assessment. We conclude by offering recommendations to support criteria-based definitions, improve risk evaluation, and reinforce the DSA's effectiveness in countering information disorders.

Keywords: misinformation; disinformation; definition ambiguity; Digital Services Act (DSA); regulation of social media platforms

Introduction

Very large online platforms have evolved into dominant infrastructures shaping public discourse (Moe, 2024; Van Dijck, 2021). In democratic societies, this raises concerns regarding the spread of misinformation and disinformation, ranked among the top global risks to public health and democracy in 2025 (World Economic Forum, 2025). The European Union has addressed these systemic risks by introducing the Digital Services Act (DSA), a landmark regulation enacted in August 2023 for very large online platforms (VLOPs) and search engines (VLOSEs). The DSA compels these actors to identify, analyze, and mitigate risks that threaten public health, security, democratic processes, and fundamental rights. It also explicitly targets content that, although not illegal, may still be misleading or deceptive, including disinformation (European Union, 2022). In February 2025, the European Commission reinforced this objective by endorsing the integration of the voluntary Code of Practice on Disinformation (CoP) into the DSA framework (European Commission, 2025).

However, the real-world implementation of the DSA has faced substantial challenges. Despite the regulatory push, its impact on mitigating misinformation and disinformation remains limited. Major platforms have rolled back previous commitments to the Code (Alvarado Rincón and Meyer-Resende, 2025), prompting criticism from civil society and researchers over persistent opacity, poor data sharing, and a lack of measurable action (Albert, 2024; Chapman, 2025; DSA Civil Society Coordination Group, 2025). Recent events, such as the annulled 2024 Romanian elections, underscore democratic vulnerabilities to problematic information flows despite the regulatory framework (John Albert, 2024; Venice Commission, 2025). Meanwhile, the DSA has become the subject of intense controversy (Jahangir, 2025), with accusations of
censorship coming from far-right political figures and leading social media platforms, despite evidence showing strong public support towards platform countermeasures addressing harmful online content (King et al., 2025).

This reveals a deeper problem: inconsistent definitions across regulators, platforms, civil society, and academia hinder systemic risk identification and shared evaluation metrics (Altay et al., 2023). Without clear criteria, platforms struggle to assess and address these phenomena, risking inadequate or counterproductive responses (Hameleers, 2024; Oversight Board, 2024; Pennycook et al., 2020).

This article explores how the absence of shared, criteria-based definitions undermines the DSA's implementation. We advance two central hypotheses. First, definitional differences are not incidental but stem from actors' diverging interests, priorities, and constraints, including regulators, platforms, fact-checkers, and researchers. Second, this lack of consensus weakens the coherence and effectiveness of platform countermeasures and hampers the overall impact of the DSA.

Drawing on a cross-sectoral analysis of seventy-nine documents, this study explores how key actors define misinformation and disinformation. It demonstrates how conflicting definitions, shaped by divergent interests, undermine the coherence of DSA implementation and fuel tensions around freedom of expression and regulatory legitimacy. While our analysis centers on European implementation, we acknowledge the broader implications of these definitional challenges given the global reach of regulated platforms and the diversity of epistemological frameworks for understanding information across different cultural contexts.

## Methodology

The corpus comprises seventy-nine documents produced by key actors involved in addressing misinformation and disinformation under the DSA's mandate to mitigate systemic risks (see supplementary material). It includes core European regulatory frameworks such as the Digital Services Act (DSA) and the Strengthened Code of Practice on Disinformation (CoP), alongside documents explicitly engaging with definitional issues, including the ERGA report and the European Democracy Action Plan. We analyzed the first transparency reports submitted by five Very Large Online Platforms (YouTube, Facebook, Instagram, TikTok, X) under the DSA for a reporting period from 25 April to 20 October 2023, supplemented by CoP-related reports and relevant internal policy documents from 1 July to 31 December 2023. The corpus also incorporates fact-checking codes from leading European and international organizations (IFCN, EFCSN). Finally, it includes fifty-seven influential academic publications, selected for their disciplinary relevance, citation impact, and institutional prominence—capturing the cross-disciplinary nature of misinformation and disinformation research while avoiding a narrow disciplinary framing.

Notably, half of the texts in our corpus lack explicit definitions of "misinformation," and thirty-three percent lack explicit definitions of "disinformation," although meanings can often be
inferred contextually. These terms are used interchangeably in ten texts of the corpus, half of which are platforms' transparency reports, effectively assigning them identical definitions.

To compare how different stakeholders define misinformation and disinformation, we conducted a qualitative manual coding of the definitions in our corpus. This process allowed us to extract each actor's specific criteria to construct their definitions. We identified twenty-five distinct criteria for misinformation and seventeen for disinformation. These criteria were then thematically grouped into four categories: (1) content quality, (2) intent, (3) underlying risk, and (4) creation and dissemination techniques. This typology provides a conceptual framework for analyzing definitional variability and enables systematic comparison of how actors approach these phenomena.

Throughout this article, we use the term information disorders as a broad umbrella concept encompassing misinformation, disinformation, and other forms of harmful or misleading content (Wardle and Derakhshan, 2017). However, we recognize that the notion of “disorder” is not ideologically neutral. As noted in previous studies exposing the pitfalls of cryptonormativity, it may reflect Western-centric assumptions about the normative functioning of information flows within liberal democracies (Banaji and Bath, 2022; Roozenbek et al., 2022; Harsin, 2024). Such framing can obscure the ways in which structurally marginalized actors may adopt alternative narratives as forms of resistance. While our primary focus is on European regulatory frameworks, we remain attentive to their broader global influence, particularly through the so-called Brussels effect. Finally, although misinformation and disinformation circulate through a variety of channels, this study concentrates on social media platforms, which are explicitly identified as key vectors of amplification in European regulatory discourse.

## Definitional Diversity as a Reflection of Stakeholder Interests

*European Regulators: Balancing Public Good and Legal Constraints.*

European regulators aim to safeguard a European vision of freedom of expression while mitigating information disorders. Their primary strategy involves enforcing frameworks like the DSA, which mandates content moderation, transparency reports, and platform accountability measures. They collaborate with platforms to ensure compliance and intervene when standards are unmet. Additionally, regulators support multi-stakeholder initiatives to assess legal frameworks and implement public awareness campaigns. Despite these efforts, budget limitations and insufficient resources restrict thorough enforcement. Tensions arise around DSA enforcement due to epistemic disagreements about freedom of speech, as European regulators try to balance this fundamental right with countermeasures to curb information disorders. Finally, jurisdictional challenges emerge, as information disorders often transcend national borders.

European regulators have adopted a definition of ‘misinformation’ drawn from Wardle and Derakhshan (2017). This definition encompasses the accuracy and reliability of the content, the absence of malicious intent, and the risk posed by the content (Table 1, “European
regulators”). Regarding ‘disinformation,’ they embrace two definitions sharing a common basis. Disinformation is distinguished from misinformation by the presence of malicious intent for strategic purposes, though one explicitly mentions amplification techniques while the other doesn’t (Table 2, “European regulators”).

These definitions reflect an effort to operationalize information disorders within a legal framework that respects freedom of expression. However, the coexistence of multiple definitions, particularly for disinformation, and the emphasis on intent enable interpretative ambiguity. This legal prudence may preserve flexibility but also risks undermining regulatory clarity and consistency, mainly when applied across borders and in collaboration with diverse platform practices.

*Social Media Platforms: Profit-Driven Engagement vs. Ethical Moderation.*

Social media platforms such as Facebook, Instagram, YouTube, TikTok, and Twitter (now X) designated as Very Large Online Platforms (VLOPs) under the DSA, serve dual roles as amplifiers of harmful content and key mitigation actors. Their engagement-driven business model, tied to advertising revenue (Ecker et al., 2022), incentivizes the spread of high-arousal content—often characteristic of information disorders—creating a structural tension between profit and public interest (Berger and Milkman, 2012; Hameleers et al., 2022; McLoughlin et al., 2024). Platform design choices, such as algorithmic recommendation and infinite scrolling, further amplify polarizing content and filter bubbles, and encourage shallow information processing (Brown et al., 2022; Ziemer and Rothmund, 2024). To avoid regulatory sanctions, platforms deploy countermeasures, including fact-checking and AI-driven moderation systems, though these systems face technical limitations in accurately detecting harmful content (IPIE, 2023; Kozyreva et al., 2024). Simultaneously, platforms engage in sophisticated lobbying and strategic communication efforts to shape regulation (Lozano and Emilia, 2025). They frame content moderation as technically complex and politically sensitive and align their positions with public interest norms, such as freedom of speech (Davidson and Lock, 2025). However, such framing often preserves opaque governance and non-binding, risk-based commitments, limiting the DSA’s capacity to mitigate systemic risks.

Transparency reports from major platforms often fail to clearly distinguish between misinformation and disinformation, frequently using the terms interchangeably—even across distinct contexts on platforms such as YouTube, Instagram, and Facebook. In contrast, X does not reference either term in its internal policies, whereas TikTok explicitly defines both (see Tables 1 and 2). This reflects a broader platform tendency to focus on content legality, despite the DSA's requirement to address misleading or deceptive content regardless of its legal status. These inconsistencies reveal deeper epistemic divergences: while European actors tend to assess content based on factual accuracy and systemic risk, U.S.-based platforms primarily adopt risk-oriented logics, relying on broader categories such as “harmful disinformation.” As a result, misinformation and disinformation are seldom treated as distinct triggers for intervention. Meta, for instance, explicitly rejects regulating content based on the category of “misinformation,” stating: “A policy that simply prohibits ‘misinformation’ would not effectively inform people using our services and would be unworkable” (Meta, n.d.).
Tables 1 and 2 summarize platforms' definitional strategies through four paradigms for misinformation and two for disinformation. Regarding misinformation, some focus on harmful misinformation, which is defined as inaccurate content related to sensitive topics that may pose individual or societal risks. Others adopt a delegated approach, relying on third-party fact-checkers to label and classify misleading content. A third perspective emphasizes technical manipulation, mainly through predefined tactics, techniques, and procedures (TTPs). Finally, some platforms reduce the issue to information reliability, avoiding normative or intent-based assessments.

Definitions of disinformation tend to be narrower and more consistent, revolving around technical manipulation and intent to deceive, often paired with public harm potential. However, the intent remains challenging to assess at scale and is rarely operationalized, blurring the boundary between misinformation and disinformation in practice.

These definitional choices often reflect platforms' balance between legal compliance and reputational risk. They defer responsibility to fact-checkers or technical categories to minimize liability and maintain flexibility in enforcement. This definitional fragmentation enables platforms to claim compliance with the DSA while avoiding clear normative commitments. As a result, disinformation and misinformation are treated as interchangeable or ambiguous categories, hindering the design of consistent countermeasures and reinforcing opacity in platform accountability.

*Fact-Checkers: Combating Misinformation with Limited Resources.*

Fact-checkers provide evidence-based corrections and raise awareness of information disorders risks (Cazzamatta, 2025; Rivas-de-Roca et al., 2024). They've established networks like the IFCN and EFCSN to ensure verification standards and bolster legitimacy. Beyond fact-checking, they focus on public education and outreach. They also collaborate with social media platforms under the Strengthen Code of Practice on Disinformation to flag and debunk misinformation and disinformation in real time (Cesarini et al., 2024). Despite progress, challenges remain. Fact-checkers face concerns about legitimacy and neutrality, accusations of bias, and limited funding, restricting their ability to scale or invest in new technologies. Meta ended its fact-checking program in the United States in January 2025, presenting it as a preliminary step before its worldwide deployment. Fact-checkers face concerns about legitimacy and neutrality, accusations of bias, and limited funding, restricting their ability to scale or invest in new technologies. While fact-checking's influence on beliefs is established, its long-term effectiveness and impact on public behavior remain contested and challenging to measure (Martel and Rand, 2024; Van Erkel et al., 2024).

The EFCSN employs the terms ‘misinformation’ and ‘disinformation’ to designate verified content resulting from fact-checking efforts, whereas IFCN does not explicitly mention these terms. Both organizations offer similar definitions of fact-checking, defined as an endeavor supported by evidence to verify the accuracy of content deemed significant to the public (Tables 1 and 2, ‘Fact-checkers’). The fact-checking process primarily focuses on the accuracy
and relevance of information to the public. However, no standard definition or criteria for
assessing the latter are specified in the examined Codes.

These observations highlight a core paradox: while fact-checkers are central actors in
identifying and labeling misinformation and disinformation, they operate without a shared
definitional framework and under significant resource constraints. This lack of consensus
becomes increasingly problematic as political tensions rise and key actors, including social
media platforms, frame their interventions as censorship (Kaplan, 2025).

*Academics: Studying the Phenomenon and Developing Long-Term Solutions.*

Academics analyze information disorders’ causes, prevalence, and consequences through interdisciplinary research from political science, communication studies, sociology, and computer science (Broda and Strömbäck, 2024). Many also develop detection tools and analytical models and publish policy recommendations. Yet, this work faces financial constraints, as data collection and tool development demand substantial funding and time. Data access remains limited despite Article 40 of the DSA, which grants vetted researchers access to non-public platform data to study systemic risks in the EU (Ohme, 2024). Platforms have also invoked European GDPR provisions to justify refusal to comply with Article 40 requirements, creating an enforcement bottleneck as academic infrastructure often cannot guarantee the data security standards platforms demand. Furthermore, escalating political tensions have subjected scholars to increasing pressures, sometimes escalating into intimidation. Finally, the complexity and evolving nature of misinformation and disinformation challenge the development of long-term solutions. Emerging trends and platforms’ rapid innovation cycles often outpace the slower, more methodical research processes, limiting the immediate influence of academic work on real-world interventions.

Definitional disagreements in academia stem from its diversity of disciplines and methodologies (Altay et al., 2024). Several publications do not explicitly distinguish between misinformation and disinformation, using the terms interchangeably or referencing only one to encompass all information disorders. Among the various definitions found in the academic literature (see Tables 1 and 2 for nine definitions of misinformation and disinformation), four main tendencies emerge.

First, misinformation and disinformation can encompass all forms of information disorders. Following an accuracy-based approach, the term “misinformation” is used more frequently than “disinformation” to refer to any false, inaccurate, or misleading information. Second, publications distinguish misinformation and disinformation as two types of information disorders differentiated by intent: misinformation is false content disseminated without the intention to cause harm, while disinformation implies a deliberate intent to deceive. Two definitions in the corpus rely on this distinction, one based solely on intent, the other incorporating potential for public harm. A third paradigm focuses on amplification techniques as a defining feature of disinformation—an approach more prevalent in platform definitions and, to a lesser extent, among European regulators. Finally, some definitions rely on a fact-checking paradigm, defining misinformation and disinformation as any content labeled as inaccurate by fact-checkers.
These variations reflect the conceptual richness of academic research but also its fragmentation.
The absence of a shared definitional framework limits the comparability of findings and hinders
the translation of academic insights into operational or regulatory strategies.

Tables 1 and 2 present a consolidated synthesis of key actors’ definitions and the associated criteria for misinformation and disinformation, respectively.
<table><caption>Table 1. Misinformation definition by actors.</caption><thead><tr><th colspan="3">Misinformation</th></tr><tr><th>Actors</th><th>Definition</th><th>Criteria</th><th>Sources</th></tr></thead><tbody><tr><td>European regulator</td><td>False or misleading content shared without harmful intent, though the effects can still be harmful</td><td><ul><li>Content quality: false, misleading</li><li>Intent: unintended / innocuous</li><li>Underlying risk: harm potential</li></ul></td><td>(European Commission, 2020) (European Commission, 2022) (ERGA, 2021)</td></tr><tr><td rowspan="2">Platforms (YouTube, Instagram, Facebook, TikTok, X)</td><td>Harmful misinformation: inaccurate, misleading, or false content that may cause significant harm to individuals or society and is related to sensitive issues</td><td><ul><li>Content quality: false, inaccurate, misleading</li><li>Underlying risk: harm potential, topic specific</li></ul></td><td>(Google, 2024) (Google, Misinformation policies) (Meta, Community Guidelines - Misinformation) (TikTok, 2024) (TikTok, Combating harmful misinformation)</td></tr><tr><td>Content that has been debunked by fact-checkers</td><td><ul><li>Content quality: fact-checked with ClaimReview tagging system (Google), as false, altered, partly false, missing context, satire and true (Meta), accurate without violation of TikTok's Community Guidelines, harmful misinformation violating TikTok's CG, can't be verified (TikTok, Combating harmful misinformation), not specified (TikTok 2024)</li></ul></td><td>(Google, 2024) (Meta, Community Guidelines - Misinformation) (Meta, 2024) (Meta, Content Distribution Guidelines) (TikTok, 2024) (TikTok, Combating harmful misinformation)</td></tr><tr><td rowspan="2">Fact-checkers</td><td>Misleading or deceptive content that has been technically manipulated and may use specific Tactics, Techniques and Procedures (TTPs)</td><td><ul><li>Content quality: misleading, deceptive</li><li>Creation/dissemination techniques: technically manipulated, TTPs</li></ul></td><td>(Google, Misinformation policies) (Meta, 2024) (Meta, Community Guidelines - Misinformation) (TikTok, 2024)</td></tr><tr><td>False or misleading content</td><td><ul><li>Content quality: false, misleading</li></ul></td><td>(TikTok, 2024, Integrity and authenticity guidelines)</td></tr><tr><td rowspan="2">Research</td><td>Misinformation is cited but not defined. Fact-checking is an evidence-based method to verify the accuracy of claims of public interest made in the public sphere.</td><td><ul><li>Content quality: inaccurate, non-authoritative source, factual/verifiable</li><li>Underlying risk: harm potential, public interest</li></ul></td><td>(EFCSN, Code of standards) (IFCN, Code of Principles)</td></tr><tr><td>False, inaccurate or misleading information</td><td><ul><li>Content quality: false, inaccurate, misleading, vague, ambiguous, verifiable/evidence-based (can be proven inaccurate), unverifiable/non evidence-based (can not be proved), untrustworthy sources, against most societally accepted evidence adjudicators, poor quality</li></ul></td><td>(Altay and al, 2023) (Apukea, Omara, 2021) (Aslett and al, 2022) (Bryanov, Vzatlysheva, 2021) (Clayton and al, 2020) (Courchesne, Ilhardt, Shapiro, 2021) (Ecker and al, 2022) (Flondi, 2011) (Garrett, Poulsen, 2019) (Guess, Lyons, 2020) (Herasimenka and al, 2023) (Jing and al, 2022) (Kozyreva and al, 2024) (Kshetri, Voas, 2017) (Lazer & al, 2018) (Losee, 1996) (Lymette, Taeihagh, 2021) (Nakov and al, 2021) (Pennycook, Rand, 2021) (Pennycook, Rand, 2022) (Peskine and al, 2023) (Renault, Restrepo - Amaries, Trousell - Clement, 2024) (Roncam and al, 2024) (Oebe, 2021) (Southwell, Thomson, Shebe, 2018) (Sphale, Beller-Arthuro, Gernau, 2024) (Valenzuela and al, 2019) (Valenzuela, Higuerma, Armonia, 2021) (Van Baavel & al, 2021) (Van der Linden, Krychenko, 2024) (Vraga, Bode, 2020) (XU, Sheng, Wang, 2021) (Zhou, Zafarani, 2018)</td></tr><tr><td></td><td>False, inaccurate or misleading information without harmful intent</td><td><ul><li>Content quality: false, inaccurate, misleading</li><li>Intent: unintended / innocuous</li></ul></td><td>(Aba-Junz, Lachlan MacKenzie, 2019) (Bateman, Jackson, 2024) (Bankier, Farris, Roberts, 2018) (Berriche, Alay, 2020) (Carone-Jack, 2017) (Hensel, 2018) (Hendricks, Veekampard, 2019) (Hernon, 1965) (Kohra, Fisher, 2012) (Kudniski and al, 2020) (Suarez-Leto, Alvarez-Galvez, 2021) (Tatila Dils, 2023) (Toder and al, 2018) (Wanless, Pamment, 2019) (Wardle, Derakhshan, 2017) (Wardle, 2019)</td></tr><tr><td></td><td>False, inaccurate or misleading information that might be intended and cause public harm (umbrella term)</td><td><ul><li>Content quality: inaccurate, unverifiable/non evidence-based can not be proved</li><li>Intent: gain driven, deceitful</li><li>Underlying risk: public harm potential, topic specific</li></ul></td><td>(McGright, Dunlop, 2017) (Saeed and al, 2022) (XU, Sheng, Wang, 2021)</td></tr><tr><td></td><td>Content that has been labeled as false, partially false or misleading by fact-checkers/ fact-checking communities (users, group pages etc...)</td><td><ul><li>Content quality: false, fact-checked as partially-true, false, misleading, impossible to judge or else/not specified</li></ul></td><td>(Dufour and al, 2024) (Mendes Rocha and al, 2021) (Miro-Linare, Aguerr, 2023) (Nakov and al, 2021) (Pennycook, Rand, 2022) (Shrish, Kotwal, 2023) (Zollo and al, 2017)</td></tr></tbody></table>
<table><caption>Table 2. Disinformation definition by actors.</caption><thead><tr><th>Disinformation</th><th>Criteria</th><th>Sources</th></tr></thead><tbody><tr><td>Actors</td><td>Definition</td><td></td></tr><tr><td>European regulator</td><td>False or misleading content that is spread with an intention to deceive or secure economic or political gain and which may cause public harm</td><td>(European Commission, 2020) (European Commission, 2022)</td></tr><tr><td>Platforms (Youtube, Instagram, Facebook, TikTok, X)</td><td>False or misleading information, disseminated with a specific intention (malicious or bad faith) that has the ability to cause certain public harms and that may use automatic amplification techniques</td><td>(ERGA, 2021)</td></tr><tr><td>Fact-checkers</td><td>Misleading or deceptive content that has been technically manipulated and may use specific Tactics, Techniques and Procedures (TTPs). Type of harmful content</td><td>(Content quality: misleading, deceptive<br>Creation/dissemination techniques: technically manipulated, TTPs<br>Underlying risk: harm potential)</td></tr><tr><td>Research</td><td>Inaccurate, misleading, or false content, intentionally shared to mislead</td><td>(Content quality: false, inaccurate, misleading<br>Intent: deceitful)</td></tr><tr><td>Fact-checkers</td><td>Disinformation is cited but not defined. Fact-checking is an evidence-based method to verify the accuracy of claims of public interest made in the public sphere.</td><td>(Content quality: inaccurate, non-authoritative source, factual/verifiable<br>Underlying risk: harm potential, public interest)</td></tr><tr><td>Fact-checkers</td><td>False, inaccurate or misleading information</td><td>(Content quality: false, inaccurate, misleading, verifiable, non-authoritative source, poor quality)</td></tr><tr><td>Fact-checkers</td><td>False, inaccurate or misleading information deliberately propagated</td><td>(Content quality: false, inaccurate, misleading<br>Intent: intended)</td></tr><tr><td>Fact-checkers</td><td>False, inaccurate or misleading information deliberately propagated with harmful intent and might cause public harm</td><td>(Content quality: false, inaccurate, misleading<br>Intent: deceitful, gain-driven<br>Underlying risk: harm potential)</td></tr><tr><td>Fact-checkers</td><td>False, inaccurate or misleading information deliberately propagated with harmful intent, using amplification techniques</td><td>(Content quality: false, inaccurate, misleading<br>Intent: deceitful<br>Creation/dissemination techniques: amplification techniques)</td></tr><tr><td>Fact-checkers</td><td>Content that has been labeled as false, partially false or misleading by fact-checkers</td><td>(Content quality: fact-checked as partially-true, false, misleading, or impossible to judge / fact-checked (not detailed) / identified by fact-checkers)</td></tr></tbody></table>
Criteria Variability in Defining Misinformation and Disinformation

Beyond institutional divergences, the ambiguity surrounding misinformation and disinformation also stems from the wide range of criteria used to define them. Table 3 presents a comprehensive list of the identified criteria, 25 for misinformation and 17 for disinformation. Further analysis allowed us to group them thematically into four categories: content quality, intent, underlying risk, and creation and dissemination techniques (Table 3). These categories provide a cross-cutting framework for systematically comparing stakeholder definitions.

<table><caption>Table 3: Framework for Defining Misinformation and Disinformation: Four Criteria Categories</caption><thead><tr><th colspan="5">Misinformation</th></tr><tr><th>Typology of Criteria</th><th>Content quality</th><th>Intent</th><th>Underlying risk</th><th>Creation/ dissemination techniques</th></tr></thead><tbody><tr><td>Cirteria</td><td><ul><li>Poor quality</li><li>False</li><li>Misleading</li><li>Deceptive</li><li>Inaccurate</li><li>Against most societally accepted evidence adjudicators</li><li>Non-authoritative source</li><li>Fact-checked as ...</li><li>Verifiable</li><li>Evidence-based</li><li>Factual</li><li>Vague</li><li>Ambiguous Unverifiable</li><li>Non-evidence-based</li><li>Untrustworthy sources</li></ul><ul><li>• Unintended</li><li>• Innocuous</li><li>• Gain driven</li></ul></td><td></td><td><ul><li>Harm potential</li><li>Public harm potential</li><li>Public interest</li><li>Topic specific</li></ul></td><td><ul><li>• Technically manipulated</li><li>• Tactics, Techniques, and Procedures (TTPs)</li></ul></td></tr><tr><th colspan="5">Disinformation</th></tr><tr><th>Typology of Criteria</th><th>Content quality</th><th>Intent</th><th>Underlying risk</th><th>Creation/ dissemination techniques</th></tr><tr><td>Cirteria</td><td><ul><li>False</li><li>Inaccurate</li><li>Misleading</li><li>Deceptive</li><li>Non-authoritative source</li><li>Factual</li><li>Verifiable</li><li>Fact-checked as...</li></ul></td><td><ul><li>• Intended</li><li>• Deceitful</li><li>• Gain-driven</li></ul></td><td><ul><li>• Public harm potential</li><li>• Harm potential</li><li>• Public interest</li></ul></td><td><ul><li>• Amplification techniques</li><li>• Technically manipulated</li><li>• Tactics, Techniques, and Procedures (TTPs)</li></ul></td></tr></tbody></table>

Drawing on the definitional data from Tables 1 and 2 and operationalizing the four categories in Table 3, we propose a visual synthesis mapping their use across key actor groups (Figure 1). This analysis reveals three main patterns. First, European regulators' second definition of disinformation is the only one that integrates all four categories. This suggests that most definitions are partial, omitting at least one of the four identified key dimensions of the phenomenon. Second, while some criteria, such as content quality, appear in nearly all definitions, others—such as intent—are selectively mobilized, especially by European regulators and academic actors. Third, the use of criteria varies substantially by actor, illustrating how institutional logic and priorities shape definitions.
<table><thead><tr><th>Actors</th><th>Misinformation</th><th>Disinformation</th></tr></thead><tbody><tr><td>European regulators</td><td><img src=" Misinformation_actors_1.png" alt="European regulators: CQ, UR, Disinformation"></td><td><img src=" Disinformation_actors_1.png" alt="Disinformation: CQ, UR, CDT"></td></tr><tr><td>Platforms</td><td><img src=" Misinformation_actors_2.png" alt="Platforms: CQ, UR, CQ, CDT, CQ"></td><td><img src=" Disinformation_actors_2.png" alt="Disinformation: CQ, UR, CQ, CDT"></td></tr><tr><td>Fact-checkers</td><td><img src=" Misinformation_actors_3.png" alt="Fact-checkers: CQ, UR"></td><td><img src=" Disinformation_actors_3.png" alt="Disinformation: CQ, UR"></td></tr><tr><td>Research</td><td><img src=" Misinformation_actors_4.png" alt="Research: CQ, CQ, UR, CQ"></td><td><img src=" Disinformation_actors_4.png" alt="Disinformation: CQ, CQ, UR, CQ"></td></tr></tbody></table>

<figure><img src="image_2.png" /><figcaption>Figure 1: Misinformation and Disinformation: Criteria Categorization by Actors</figcaption></figure>

## Content Quality

Content quality is the fundamental criterion for defining misinformation and disinformation. It is based on truthfulness ('false,' 'inaccurate'), misleadingness ('misleading,' 'deceptive'), source credibility ('non-authoritative source'), or fact-checking outcomes.

Most definitions focus on information truthfulness or deceptive nature without clarifying how these criteria are assessed. Fact-checkers seek legitimacy and are the only actors to provide detailed evaluation processes, focusing on factual, verifiable claims and source quality. However, methodological differences remain between institutions, as the IFCN and EFCSN do not mandate specific methodologies. Academic researchers prioritize fact-checking for both phenomena, while platforms apply this framework only to misinformation, reflecting academics' pursuit of methodological rigor versus platforms' efforts to reduce liability while maintaining neutrality.

## Intent

European regulators and some academic definitions use intent to define misinformation and disinformation. Among social media platforms, only TikTok includes intent in its definition of 'disinformation,' framing it within an educational context. In contrast, fact-checkers avoid this criterion and focus on quality and underlying risk.

The intent is assessed through willingness ('unintended,' 'intended'), intent to deceive or harm ('deceitful'), or the pursuit of gain, whether economic, political, or psychological ('gain driven'). This category appears more frequently in disinformation definitions (6/10) than misinformation (2/10), reflecting misinformation's broader use as an umbrella term for unintentional inaccuracy.
European regulators incorporate the concept of intent to address systemic risks and enable legal sanctions, while academic research examines it to explore the creation and spread of disinformation. Fact-checkers and platforms likely avoid intent to reduce threats of defamation.

## *Underlying Risk*

This category appears in at least one definition across all groups, though inconsistently applied. Underlying risk is assessed by considering the nature of the subject ('public interest,' 'topic-specific'), its reach ('public interest'), and its potential for harm ('harm potential,' 'public harm potential').

European regulators include this category in their definition of misinformation and disinformation, in line with the DSA. Fact-checking organizations consider it to prioritize claims for verification due to resource and time constraints. Social media platforms also refer to this criterion to meet regulatory obligations and justify countermeasures such as labeling or downranking content.

In contrast, academic research includes underlying risk in only one of its definitions of misinformation and disinformation. This reflects the ongoing exploration of risks associated with information disorders, complicated by diverse impact measures and the challenge of establishing causal links. These concepts are often treated as meta-risks, with their consequences manifesting indirectly (Van der Linden et al., 2024).

## *Creation and Diffusion Techniques*

Finally, creation and dissemination techniques appear in social media platforms, European regulators, and academic research definitions, but are absent from fact-checkers' criteria categories. They encompass Tactics, Techniques, and Procedures ('TTPs'), technical manipulation ('technically manipulated'), and amplification techniques.

Platforms emphasize these elements in their internal policies and through frameworks like the Strengthened CoP, where such criteria serve primarily operational purposes. These approaches draw on cybersecurity models and automated detection systems, aligning with platforms' need for scalable, enforceable moderation tools.

European regulators and academic sources specifically associate these techniques with disinformation, often linking them to intent and coordinated strategies, such as producing cheap fakes or deepfakes (Hameleers et al., 2024; Paris and Donovan, 2019). The regulators' attention to amplification methods also aligns with broader legislative concerns, such as those addressed in the AI Act.

In contrast, academic studies beyond computer science have paid limited attention to creation and dissemination techniques, possibly due to disciplinary boundaries or limited data access. Fact-checkers, for their part, rely on manual analysis and journalistic expertise, with ongoing debates around the ethical integration of automated detection tools into their workflows.

Our analysis confirms the absence of a shared definitional framework for misinformation and disinformation across—and even within—stakeholder groups. These divergences go beyond terminology and reveal competing priorities, epistemological approaches, and institutional constraints. They shape how systemic risks are interpreted, what content is targeted, and how
responsibility is distributed. The next section examines the practical consequences of this fragmentation and the main risks arising from the lack of a harmonized definition.

## How Definitional Diversity Undermines DSA Enforcement

Despite the frequent recognition of the lack of consensus on the definitions of misinformation and disinformation (Bateman and Jackson, 2024; Pennycook and Rand, 2022; Van der Linden and Kyrychenko, 2024), the operational and cascading effects of this definitional ambiguity on the implementation of the DSA remain, to the best of our knowledge, underexplored. This section identifies five core areas where definitional ambiguity presented in the previous section undermines DSA implementation and creates risks.

First, inconsistent definitions create tension at the intersection of regulation and freedom of expression, where moderation is perceived as censorship. Second, they limit researchers' and independent experts' ability to effectively audit systemic risks as specified in DSA Articles 37 and 40. Third, these ambiguities hinder the development of effective countermeasures by allowing for different interpretations of platforms' obligations. Fourth, they reduce the effectiveness of interventions, including detection systems that require clear parameter definitions. Finally, they complicate impact assessment, as the lack of a consistent baseline makes it challenging to evaluate whether platforms are meeting the DSA's systemic risk mitigation goals. By mapping these interrelations, we demonstrate how definitional inconsistencies undermine the regulatory coherence and practical effectiveness of the DSA framework (Table 4).
<table><caption>Table 4. Risks and Limits of Definitional Diversity</caption><thead><tr><th>Risks and limits typology</th><th>Global risks and limits</th><th>Detailed risks and limits</th><th>Sources</th></tr></thead><tbody><tr><td rowspan="2">Freedom of expression</td><td>State censorship</td><td>Criminalizing misinformation/disinformation in overly broad terms can restrict the public's freedom of expression.</td><td>(European Commission. 2020) (Van Bavel and al. 2021) (Tambini. 2017) (Altay and al. 2023) (Human Rights Watch. 2021) (Reporters Without Borders. 2017) (Van der Linden, Kyrychenko. 2024) (Bateman, Jackson. 2024) (World Economic Forum. 2024)</td></tr><tr><td>Digital platform censorship</td><td>Content moderation based on an overly broad definition of misinformation/ disinformation may restrict freedom of expression</td><td>(Van der Linden, Kyrychenko. 2024)</td></tr><tr><td rowspan="2">Research</td><td rowspan="2">Research freedom and safety issues</td><td>Journalists or researchers defining misinformation/disinformation based on intent risk facing legal and reputational repercussions</td><td>(Jack. 2017)</td></tr><tr><td>Malicious actors use the lack of clear definitions to turn the misinformation/ disinformation accusation against researchers, journalists or civil society, and undermine efforts to combat disinformation.</td><td>(ERGA. 2020) (Bateman, Jackson. 2024) (Ecker and al. 2024)</td></tr><tr><td rowspan="2">Counter-measures design</td><td>Interdisciplinary collaboration difficulties</td><td>Divergent definitions across disciplines impede collaborative countermeasure development.</td><td>(Wardle, Derakhshan. 2017) (Altay and al. 2023) (Khan, Brohman, Adas. 2022)</td></tr><tr><td>Lexical choices effects</td><td>The naming of misinformation/disinformation phenomenon influences understanding and thus shapes appropriate countermeasure strategies</td><td>(Wardle, Derakhshan. 2017) (Wanless, Pamment, 2019)</td></tr><tr><td rowspan="15">Counter-measures efficiency</td><td>International coordination struggles</td><td>Highly unharmonised approaches regarding misinformation/disinformation creates challenges for transborder services</td><td>(ERGA. 2020)</td></tr><tr><td rowspan="3">Overlooked forms of disinformation/ misinformation</td><td>Defining misinformation/disinformation according to a true/false dichotomy (narrow definition) does not capture the insidious forms of this phenomenon therefore limiting the counter-measures scope (ex: fact-checking or fact-checking based counter-measures)</td><td>(ERGA. 2020) (Van der Linden, Kyrychenko. 2024) (Altay and al. 2023) (Søe. 2017) (Guess, Lyons. 2020)</td></tr><tr><td>Defining misinformation/disinformation according to the use of AI technology does not capture the insidious forms of this phenomenon leaving some misinformation/disinformation content online.</td><td>(Conseil de surveillance. 2024)</td></tr><tr><td>Backfire effect</td><td>Limited implementation of certain countermeasures can undermine their effectiveness by legitimizing unaddressed misinformation/disinformation content</td><td>(Pennycook and al. 2020)</td></tr><tr><td rowspan="2">Non-practical definitions</td><td>Although widely used, the definition of misinformation/disinformation according to intention is proving difficult to put into practice and may therefore be ineffective regarding the conception/implementation of new counter-measures</td><td>(Guess, Lyons. 2020) (Bateman, Jackson. 2024) (Pennycook, Rand. 2021) (Jack. 2017) (Wardle, Derakhshan. 2017) (Karlova, Fisher. 2012) (Wanless, Pamment. 2019)</td></tr><tr><td>A too-broad definition blurs the line between acceptable and unacceptable content, hindering effective countermeasures against misinformation/disinformation (ex: user based counter-measures)</td><td>(Wanless, Pamment. 2019)</td></tr><tr><td>Challenges in designing automated detection systems</td><td>Unharmonized definitions of misinformation/disinformation complicate the practical implementation of the concept, thus hindering automated detection of relevant content.</td><td>(Søe. 2017) (Faye and al. 2024) (Peskine and al. 2023) (Pennycook, Rand. 2021) (Miró-Linares, Aguerri. 2023) (XU, Sheng, Wang. 2021)</td></tr><tr><td>Lack of harmonized labeling procedures for fact-checking</td><td>Varying content evaluation methods among fact-checking organizations and digital platforms hinder consistent countermeasure implementation and trust among the public</td><td>(Miró-Linares, Aguerri. 2023) (Jack. 2017)</td></tr><tr><td>Pitfalls of relativism</td><td>Defining misinformation according to a true/false dichotomy leads to controversies that can tend towards relativism.</td><td>(Guess, Lyons. 2020) (Bateman, Jackson. 2024) (Varga, Bode. 2020) (Southwell, Thorson, Sheble. 2018) (Wanless, Pamment. 2019) (Ecker and al. 2024)</td></tr><tr><td rowspan="2">Impact assessment</td><td rowspan="2">Impact assessment difficulties</td><td>Unharmonized definitions hinder assessing the scale of the misinformation/disinformation phenomenon</td><td>(Altay and al. 2023) (Rogers. 2020) (Pennycook, Rand. 2022)</td></tr><tr><td>Unharmonized definitions hinder assessing the impact of dedicated policies or counter-measures (DSA, platforms responses...), partly due to a lack of dedicated metrics</td><td>(Altay and al. 2023) (Rogers. 2020) (IPIE. 2023) (Meta. Community Guidelines)</td></tr></tbody></table>
Freedom of expression is among the most pressing concerns. The diversity of definitions allows opposing views of freedom of speech to coexist, creating regulatory tensions. The North American perspective focuses on underlying risks without considering content's objectionability (Banaji and Bhat, 2022), leading to strong opposition to DSA regulation and increased systemic risks. Conversely, in authoritarian regimes, definitional ambiguity can empower authorities to wield significant influence over the definition of truth, particularly in contexts where misinformation or disinformation may be employed as a form of resistance. Consequently, dissenting viewpoints can be labeled as false and removed under the guise of combating information disorders (Altay et al., 2023; Van Bavel et al., 2021).

On platforms, the absence of consistent definition and proportionate moderation frameworks can result in uneven enforcement. For instance, altered content depicting public figures doing things they never actually did can remain online (Oversight Board, 2024), while satire is occasionally flagged as false information (see Figure 2). Likewise, insufficient moderation can lead to backlash, causing users to leave platforms like X due to hate speech and disinformation allowed by the erosion of content governance (Bisbee and Munger, 2025).

These inconsistencies undermine trust and fuel narratives framing regulation as censorship, despite the DSA's aim to protect free expression.

<figure><img src="image_3.png" /><figcaption>Figure 2. 1) Altered video of U.S. President Joe Biden neither removed nor signaled by Meta; Oversight Board upheld the decision; 2) Satirical Instagram content by the French media outlet Topito flagged as potential misinformation (collected by the authors).</figcaption></figure>
Definitional ambiguity constrains academic research, particularly regarding the intent-based distinction between misinformation and disinformation, which poses epistemological and methodological challenges. In digital environments, intent is difficult to prove and rarely explicit, exposing researchers to criticism, reputational damage, or legal threats (Caroline Jack, 2017). Conversely, some actors exploit this ambiguity to delegitimize scientific work as ideologically motivated (Ecker et al., 2024). These dynamics hinder evidence-based research into information disorders and limit independent experts' capacity to audit VLOPs and VLOSEs under DSA requirements (Napoli, 2025).

Countermeasure development suffers from definitional inconsistency. Lexical and definitional variations shape strategic responses: truth-based definitions favor fact-checking and content labeling, while technical framings, based on content manipulation or inauthentic behaviors, emphasize algorithmic detection. The core challenge is not diverse approaches but definitional incoherence, which undermines effective policy development. This ambiguity allows platforms to exploit regulatory gaps under the DSA, maintaining compliance through variable interpretations while evading substantive accountability.

Definitional ambiguities compromise intervention effectiveness. Automated detection systems require precise definitional boundaries to classify content; without them, false positives and missed detections proliferate, weakening moderation outcomes. Similarly, over-reliance on content labeling can produce an “implied truth effect,” where unlabeled content is perceived as accurate (Pennycook et al., 2020). Moreover, definitions that focus exclusively on intent or binary notions of truth and falsity tend to overlook more subtle forms of harmful content, thereby narrowing the scope of intervention and enabling such content to circulate unchecked (Allen et al., 2020; Van der Linden and Kyrychenko, 2024). These limitations undermine platforms’ ability to implement robust and proportionate responses to information disorders as required by the DSA-compliant responses.

Finally, the lack of definitional harmonization undermines impact assessment and policy evaluation. These discrepancies hinder compatibility and interoperability across research efforts and lead to inconsistent and contradictory findings. Without a consistent framework, assessing the effectiveness of social media countermeasures becomes difficult, complicating both outcome evaluation and the formulation of coherent policy recommendations. This challenge is particularly acute under the DSA, where platforms must demonstrate mitigation of systemic risks that may stem from disinformation. Yet, without clear criteria to identify information disorders online, there is no reliable baseline for evaluating progress.

## Operationalizing Definitions: Recommendations for Strengthening the Implementation of the DSA

This final section provides practical recommendations for operationalizing the four identified definitional criteria (i.e. content quality, intent, underlying risk, and creation and dissemination techniques) and strengthening the DSA's coherence and enforceability. It is worth noting,
however, that improving definitional clarity is a necessary, though not sufficient, condition for
effective regulation. Its impact ultimately depends on broader structural factors, including
political resistance, institutional constraints, and uneven enforcement capacities, which are
further discussed in the concluding section.

Content quality

Assessing content quality requires a standardized evaluation framework recognized and adopted at the European level. Such a framework could draw on fact-checking methods and assessment guidelines. Pedagogical and transparent efforts by entities like Science Feedback (sciencefeedback.org) could serve as valuable reference points. Scholars have previously advocated for an international framework (Lewandowsky et al., 2017). However, to ensure feasibility, such an evaluation system should first be integrated into current European regulations, such as the CoP or DSA. This approach could help ensure that regulatory actors agree on the same epistemic values. The content quality assessment framework would provide explicit criteria aligned with European Union ethical principles, fostering consensus among diverse stakeholders while maintaining democratic legitimacy.

Intent

Despite its centrality, intent remains difficult to assess, and few studies have explored practical
evaluation methods (Lewandowsky et al., 2024). Existing approaches often rely on privileged
resources, such as internal platform data or advanced NLP tools, underscoring the need for
new, accessible applications to support timely analysis. Advancing this field will require closer
cross-disciplinary collaboration to develop workable, scalable tools.

We also recommend addressing the current lack of terminological consistency. The
interchangeable use of ‘misinformation’ and ‘disinformation’—sometimes employed as both
specific and umbrella terms—illustrates broader conceptual confusion. For instance, the
Strengthened CoP distinguishes “Disinformation” (capitalized) as a generic category, while
“disinformation” refers strictly to intentional content, a nuance that risks inconsistent
interpretation. Although the term information disorder was proposed to bridge this gap, it
remains underutilized and carries normative assumptions about ideal information flow.
Standardizing terminology across actors and contexts should be prioritized to support coherent
regulatory and research efforts.

Underlying risk

The DSA aims to “effectively identify and mitigate the risks and the societal and economic
harm that may arise.” This risk-based approach defines a countermeasure framework while
minimizing over-moderation. Nevertheless, DSA risks remain subject to interpretation. Critical
topics such as climate risks, ranked second most significant risk by the World Economic Forum
for 2026, lack explicit DSA recognition. Direct legislative recognition could enhance platform
responses to climate misinformation and disinformation, currently deemed inadequate by
independent observers (Climate Action Against Disinformation, 2024).

Like the AI Act, the DSA would benefit from a framework explicitly linking risk levels to
specific information disorder types, considering subject matter, misleadingness, or other
relevant factors to establish research-based countermeasures. This would ensure proportionate,
context-tailored responses while preserving Union citizens' fundamental rights, including freedom of expression.

### *Creation and dissemination techniques*

Creation and dissemination techniques alone cannot define misinformation or disinformation, as much content—such as simple posts, tweets, or out-of-context images—lacks identifiable production markers. Moreover, information disorders often spread via “super-sharers” (Baribi-Bartov et al., 2024; Van der Linden and Kyrychenko, 2024): political influencers, media figures, or vaccine skeptics who leverage networks and recommendation algorithms for viral reach without artificial amplification (DeVerna et al., 2024). While identifying such techniques supports detection, their role in European regulation should be to inform automated tools—not serve as the sole definitional basis.

## Conclusion

This article highlights how definitional ambiguity hinders DSA implementation. Analyzing seventy-nine documents from regulators, platforms, fact-checkers, and researchers, we identify four definitional criteria for misinformation and disinformation: content quality, intent, associated risk, and creation and dissemination techniques. Our framework shows how divergent institutional logics fragment definitions, creating regulatory blind spots with implications in five critical areas: freedom of expression, research, countermeasure design, intervention effectiveness, and impact assessment. Finally, we provide recommendations for operationalizing definitional criteria supporting DSA enforcement.

Definitional inconsistencies represent one component of broader DSA implementation barriers. Limited regulatory capacity stems from uneven Digital Services Coordinator resourcing, varying member state commitment, and budgetary constraints complicating consistent enforcement. Improving definitional clarity could enhance legal certainty but requires complementary institutional support, sustainable funding, and political commitment. Addressing these dimensions collectively is essential for realizing the DSA's systemic risk mitigation goals.

Stakeholders' willingness to revise their definitional criteria may vary significantly. Social media platforms are least likely to adopt restrictive criteria, as vague definitions protect their legal and commercial interests. While DSA regulatory pressure may force selective alignment, platforms generally resist changes that could increase liability or limit algorithmic flexibility. However, civil society mechanisms under the DSA, such as the whistleblower tool and formal complaint procedure, represent an untapped potential that could influence platform alignment. European regulators are more inclined to adapt definitions as implementation challenges emerge, though political negotiation and institutional fragmentation slow progress. Fact-checkers, reliant on credibility, could harmonize criteria to improve detection while shielding against censorship and liability. Academics could also use such protective mechanisms, though disciplinary boundaries and varied research agendas limit convergence. Overall, while definitional harmonization is unlikely to occur uniformly or voluntarily, incremental alignment
may emerge around shared operational needs—particularly where regulation, research, and platform accountability intersect.

Our approach presents methodological limitations. First, although we gather heterogeneous sources, we built a multidisciplinary and cross-sectoral corpus reflecting definitions informing DSA implementation, prioritizing European representativity to capture key institutional perspectives. Second, VLOPs' international reach implies potential issues beyond the scope of our EU-focused analysis, including definitional and epistemic inconsistencies that could impede legal framework interoperability and research comparability on a global scale. Future research should investigate the collaborative development of standardized definitions and their incorporation into European platform governance mechanisms. Priority areas include assessing how shared criteria could enhance algorithmic countermeasure accountability and exploring umbrella terms with graded intervention thresholds for unclear intent situations.

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