The disinformation lifecycle: an integrated understanding of its creation, spread and effects

Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Discover Global Society Research The disinformation lifecycle: anintegrated understanding ofits creation, spread andeffects KimberleyKruijver1· NeillBoFinlayson2· BeatriceCadet3· SicovanderMeer2 Received: 3 February 2025 / Accepted: 20 May 2025 © The Author(s) 2025 OPEN Abstract The proliferation and development of social media platforms in recent years has contributed significantly to the spread of disinformation. Police Authorities around Europe have observed that harmful or criminal behaviour, stemming from social unrest, hate speech, and violent disorder are regularly preceded by disinformation campaigns. This begs the question: How can practitioners be better prepared for the real-world consequences of malign disinformation activities and to potentially even mitigate any criminal consequences? The first step in properly countering disinformation is to enhance the understanding of the complex phenomenon. Therefore, this article puts forth a new theoretical framework, called the ‘C5 Interaction Model’, that explains the creation, spread and impact of disinformation, synthesising academic theory to provide practical guidance on disinformation dynamics. The multidisciplinary model represents a lifecycle and contains five main elements: Context, Causes, Content, Consequences, and Cycle of Amplification. They are each organised into two further layers of (sub)factors, which were developed to provide a comprehensive overview and breakdown of the important elements of disinformation. The C5 Interaction Model represents one of the first concerted efforts to bring diverse insights together into a comprehensive integrative framework. The complexity of the model shows that this process is non-liner and that there are a multitude of factors determining the lifecycle of disinformation, making it a highly complex phenomenon to research. A key contribution of this article is the focus on the interaction between different elements that influence the process of disinformation—from creation to consequences. Importantly, the lifecycle route is predominantly influenced by the social context in which it exists. 1 Introduction The rapid development of social media platforms and online social networks in the past decade has changed the way people communicate with each other. Users rely on these tools to share information, connect with other people, and stay informed about trending events. Despite the potential benefits of this, social media platforms have also contributed to an explosive growth in the amount of false and inflammatory information being spread in the world. Nowadays, the presence of such falsehoods online—referred to as disinformation from hereon—is not only disruptive or distracting for everyday users, but it can also have an impact on the individual and behavioural level, contributing to harmful or even criminal behaviour on the part of the receivers of the disinformation messages. For example, during the COVID-19 pandemic, false information was widely shared that undermined trust in public institutions across several countries. This not only led to (sometimes unlawful) protests, but also to vandalism of, for example, critical infrastructure [1]. Research * Kimberley Kruijver, [email protected]; Neill Bo Finlayson, [email protected]; Beatrice Cadet, Beatrice-cadet@ hotmail.fr; Sico vander Meer, sico[email protected] | 1Netherlands Organisation forApplied Scientific Research (TNO), TheHague, Netherlands. 2TNO, TheHague, Netherlands. 3Air France KLM, Paris, France. Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 into the German ‘Reichsbürger’ movement showed that exposure to disinformation narratives can even impact an individual’s justification of the use of violence against the out-group [2]. Disinformation can thus have a profound impact on both individuals and wider society. Firstly, it is important to explain what is meant by disinformation. The European Union (EU) High Level Group on Fake News and Online Disinformation defines it as “false, inaccurate, or misleading information designed, presented and promoted to intentionally cause public harm or for profit” [3, 4]. Debate continues in academic literature about the definition, conceptual boundaries and relevance of this distinction between misand disinformation. However, a practical distinction may be that disinformation is generally created and spread for economic gains or for political or ideological goals, and it can be exacerbated by how different audiences and communities receive, amplify, and engage with disinformation [3]. This is different to falsehoods being spread in good faith by people concerned about health risks, vaccines or technology, for example. The risk of disinformation includes threats to democratic political processes and values, which can specifically target a variety of sectors, such as health, science, education, finance and more. What makes disinformation especially harmful is that it can take the form of both a single message as well as multiple interrelated messages, such as a targeted disinformation campaign. Police Authorities (PAs) have observed that criminal activities such as hate speech, violence, riots, and terrorist attacks are regularly preceded by disinformation campaigns. An infamous example is the Capitol riots in the United States (US) in January 2021. A disinformation campaign that increasingly gained attention on Facebook, claimed that former President Donald Trump was working against a so-called ‘criminal deep-state’ [5]. The disinformation alleged that members of this deep state, elite associates of the US government, media, and business, were actively trying to undermine him. Within this context, it was claimed that President-Elect Joe Biden had ‘stolen’ the election from his opponent (and then President) Donald Trump by election fraud. This narrative eventually led to a crowd of rioters storming the Capitol building in Washington DC during a joint session of Congress to formalise Biden’s election victory. Five people were killed in the riots, including a police officer, and many more people were injured, including 135 police officers. Afterwards, questions arose aboutwhether the police were adequately prepared for the riots [6]. The impact of disinformation in stoking public disorder and violence in the US has been felt across the world. Based on interviews with five European PAs, a shared need was formulated to improve readiness for the harmful or even criminal effects of disinformation on the local level.1 At present, PAs tend to be short on staff with relevant knowledge on the disinformation process, which is fundamental when seeking to counter its damaging effects on social order, safety and security. Therefore, the first step in aiding not only these PAs, but practitioners tasked with handling the real-world consequences of disinformation more generally is to enhance their understanding of the complicating dynamics around disinformation, grounded in academic theory, while at the same time providing practical guidance. This article was written within the context of the EU funded research project VIGILANT which aims to develop an integrated platform of advanced disinformation identification and analysis tools to cover disinformation from major sources.2 1.1 Literature review Attempts have been made to provide guidance and clarity on the threat of disinformation for practitioners, who are often tasked with dealing with its real-world impact. For example, the RAND Corporation published guidelines for PAs on how to combat misinformation particularly around the COVID-19 pandemic [7], while the EU issued a handbook to provide local and regional authorities with recommendations on how best to counter disinformation [8]. These publications focus on countering misor disinformation, but provide little understanding of its underlying mechanisms, how it spreads, and its consequences. Furthermore, the guidance offered in these publications is rather practical and therefore does not describe the necessary insights from academic literature to adequately understand the behavioural and cognitive drivers of disinformation. An academic handbook on disinformation, edited by Arcos, Chiru and Ivan [9], does offer such insights into the dynamics of disinformation. However, for all that it offers in knowledge on the subject, it in turn misses the required accessibility and applicability for use by practitioners. As such, this provides an opportunity to build 1 These interviews were carried out within the context of the VIGILANT project. 2 The EU VIGILANT project aims to address these issues by developing an integrated platform of advanced disinformation identification and analysis tools to cover disinformation from major sources, in all modalities, and in multiple languages. Functioning as a thorough theoretical base of the complete VIGILANT project, this article develops a conceptual framework of disinformation, which provides conceptual input for the technological tools that are developed in other work packages of the project. The model may also help prevent overlooking relevant aspects of disinformation in later phases of the project. For more information about VIGILANT, see: https:// www. vigil antpr oject. eu/. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research on this work by developing accessible and practical guidance on the dynamics of disinformation that strikes a balance between theory and practice. There are numerous frameworks and models that attempt to make sense of the disinformation phenomena in academic literature. Owing to the relative novelty of this field of research, there is a strong line of research on developing taxonomies and typologies for concepts relating to disinformation (e.g. [10]), while others have sought to provide frameworks for the multimodal nature of disinformation content [11]. Indeed, much of the recent academic research on disinformation has focused on the technical aspects of detection, whether it is employing language models to design multimodal detection systems (e.g. [12]) or more theory-driven detection models that analyse writing features of content (e.g. [13]). Furthermore, steps are being taken to develop more sophisticated computational [14] or automated [15] frameworks and methods for detection. Although these are useful technical solutions for detecting and analysing disinformation content, they do not provide a satisfactory theoretical explanation of the dynamics that underpin the creation, spread and effect of disinformation. One of the most comprehensive frameworks on disinformation is DISARM (DISinformation Analysis & Risk Management): an open-source framework designed to identify and counter disinformation [16]. It provides both a conceptual and practical understanding of disinformation by comprising a structured methodology for identifying, analysing, and mitigating the tactics and techniques used by proponents of disinformation [17]. Similar to this, Kozyreya etal. [18] developed a toolbox of individual-focused interventions which provides a comprehensive conceptual overview of how to counter misinformation. Both frameworks achieve an effective synergy of theory and practical guidance. However, the focus remains on the what (detecting and countering disinformation) and not the why (underlying mechanisms of disinformation spread). An understanding of the latter is precisely what practitioners need in order to more effectively carry out the former. There is a considerable body of research in the social and behavioural sciences that provides frameworks for analysing the underlying drivers of disinformation. For the most part, however, these relate to the dissemination or diffusion of disinformation in online social networks (e.g. [19]), often employing computational methods (e.g. [20]). That said, Froehlich [21] developed a framework that gives insight on the creation, dissemination and effects of disinformation. Yet, he focuses specifically on the legitimation and sustainment of disinformation,how people’s own critical thinking and the power of cognitive authorities can sustain belief in ‘fake news’.3 Similarly, Arayankalam and Krishnan [22] attempted to synthesise this rather fragmented body of research on disinformation through a systematic literature review, focusing particularly on the “psychosocial antecedents of its spread” and subsequent impact. The authors conclude, based on their review, that further research is needed to establish a much-needed theoretical and methodological grounding for understanding disinformation, highlighting the importance of taking into account behavioural, social and environmental factors [22]. The framework developed by George, Gerhart and Torres [23] does provide a comprehensive multi-disciplinary theoretical grounding for the underlying dynamics of the creation (including the underlying motives) and subsequent spread of disinformation for ‘fake news research’. The authors focus on (1) distinct relationships that cause the message to accelerate, perpetuate and eventually cause societal impact,and (2) the roles that different actors—like creators and consumers—play. Although this framework helpfully illustrates the main elements of a general disinformation process, some important gaps were identified. The importance of context in the process was lacking. According to Hameleers [24], context is key when it comes to understanding the actors, intentions, and techniques behind disinformation. For instance, it is important to consider the role that different threats, political challenges, media ecosystems and specific events can have on the spread and impact of disinformation such as, for example, the current hybrid threat environment [25].4 Moreover, the description and framing of the real-world (often criminal) implications of disinformation in George etal.’s [23] framework miss the nuance and detail required to adequately explain the escalatory reinforcing relationships between the factors that underpin the dynamics of disinformation. Evidently, multiple disinformation frameworks exist from various disciplines such as communication studies, psychology, political science, sociology, and computer science. This plethora of research carries the risk of fragmentation, as researchers often focus on isolated aspects of the problem. There is a gap in thecurrent literature on disinformation theory for a truly multidisciplinary conceptual or theoretical framework for understanding the causes, contents and 3 Disinformation is often referred to as ‘fake news’, but this is an inadequate term because disinformation often involves content that is not completely ‘fake’ but fabricated information blended with facts and practices that go well beyond anything resembling ‘news’ [3]. Nevertheless, the present article includes the review of literature using the term ‘fake news’ in addition to other terms for disinformation precisely because it is often incorrectly used as a term for disinformation in general. 4 For more information on context, see chapter3.1 Context. Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 consequences of disinformation that incorporates behavioural, cognitive and communication science perspectives. Furthermore, as mentioned previously, there is a need for such a framework to be practicable and applicable for laymen or practitioners, particularly those on the front line of safety and security work. Given these gaps, the purpose of this paper is to develop a comprehensive framework that explains the creation, spread and impact of disinformation, synthesising academic theory to provide practical guidance for practitioners. 1.2 Research problem There is need for greater understanding, knowledge and expertise about disinformation within the authorities tasked with dealing with the real-world consequences of the phenomenon. Based on the practical and theoretical discussions as described above, this article sets out to answer the following research question: “Which conceptual elements constitute the disinformation process that can escalate to harmful or even criminal behaviours?”. It aims to contribute to (1) practice, by aiding practitioners to understand and thereby being able to respond adequately to disinformation, as well as to (2) theory, by developing a conceptual model that demonstrates the relationships between the different elements of disinformation and how its impact is affected by diverse factors. The following chapter explains the research methodology. In Chapter3, the developed ‘C5 Interaction Model’ is presented, including a detailed description of its various elements. Chapter4 discusses the findings, categorised along two lines: its academic contribution and its practical relevance, as well as some limitations. The article ends with Chapter5, which includes the conclusions of this research report. 2 Methodology With the aim of collecting as many key elements of the disinformation phenomenon as possible, the developed C5 Interaction Model (discussed in Chapter3) is primarily based on insights acquired from existing academic literature from various disciplines, including communication and media studies, sociology, and psychology. The first step in the literature review was to build the basic foundations of the model, such as the main factors in the process of disinformation and how they relate to each other.5 To limit the number of articles to be scanned, the focus of this part of the review was on existing literature reviews and meta-analyses because they already offer a comprehensive review of a certain academic field and ensure a state-of-the-art analysis. This methodological choice can be seen as a limitation because already synthesized data was re-analysed again. However, due to time constraints this was still preferred and the newly combined data was nonetheless able to create something new. Keywords such as ‘review disinformation causes’, ‘disinformation review content’ and ‘fake news riots’ were used to search relevant literature; mainly via Google Scholar. Moreover, when considered useful, stand-alone academic papers and related literature were also included through snowballing, a common activity in literature research that consists of identifying new articles quoted in the papers already analysed. The list of academic articles includes purely theoretical papers as well as more practical ones, including case studies. The data was then analysed using the Grounded Theory Method [26]. This method ensures that new theories or models are built based on the systematic collection of data. By using a coding system, the literature was thematically assembled into groupings of articles related to the same aspect of disinformation. For instance, an article providing insights on the platforms used to create and/or spread content was coded under “Channels”, which was subsequentially categorised under the code “Content”. This thematic analysis allowed the authors to break down and group different aspects of the collected literature. To measure inter-rater reliability, the coding was carried out in two main phases, whereby the codes were checked by another author and then confirmed or discussed in order to adjust where appropriate. While no formal inter-rater reliability coefficients (e.g., Cohen’s Kappa) were used, coding was iteratively refined through collaborative discussions aimed at achieving consensus, following principles of reflexive thematic analysis [27]. Coding was also facilitated by corresponding it with five initial use cases6 provided by the PAs to the VIGILANT project, which ensured the coding remained relevant for practical application. A questionnaire was created and organised into four parts: (1) 5 51 articles were analysed for specifically the conception of the model. For reporting purposes, a total of 120 articles were used, including the 51 of the initial literature review. Additional references were for instance added to justify the methodology or the background information of the completed work. 6 Five PAs are a members of the VIGILANT project: Catalonia (Spain), Estonia, Germany, Greece and Moldova. Each of them provided a use case of a realistic scenario in which they encountered disinformation. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research description about the use case, (2) questions about relevant factors, (3) the relationship to criminal behaviour, (4) the investigative process, and (5) the (technical) support they would have wanted. In addition, two use cases in collaboration with the Catalonian police were written following the same questionnaire as well as three additional use cases were created based on open-source information about (1) The Dutch Willem Engel Court case, (2) The US Capital riots, and (3) a New Zealand court case on disinformation. These ten distinct and structured use cases fed information to the authors’ understanding on different disinformation processes. This iterative process, completed as part of the Grounded Theory Method, allowed the authors to gradually construct the conceptual model. Once this had been completed, steps were taken to validate the model by applying it to a disinformation use case developed in conjunction with the Catalonian Police within the VIGILANT project. The main codes became clear quite early in the analysis process as articles and codes were thematically grouped, and later also became the main elements of the C5 Interaction Model: Context, Causes, Content, Consequences, and Cycle of Amplification. Causes, Content, and Consequences had already been suggested by the experts engaged in the proposal phase of the VIGILANT project, which were reflected in meta-analysis articles like George etal. [23] and Arcos etal. [25]. As a result of the iterative literature review, the codes Context and Cycle of Amplification were identified as main contributing factors. First, Context was added as multiple articles referred to contextual factors such as social context [28], the hybrid threat environment [25] and national policy and legal differences with regards to disinformation [29]. Since the context is often neglected in disinformation research [22], even though it is vital in influencing the path of disinformation messages, it is one of the two codes that were chosen to emphasize more. The second code is the Cycle of Amplification. This was mainly inspired by George etal. [23], who created a framework to help drive future research on disinformation. After finding what George etal. [23] refer to as the cycle of amplification, the authors recognized (parts of) that element in other articles as well. For example, when Zhang and Ghorbani [28] refer to social context, they actually argue that it heavily influences to what extent messages are disseminated, hence to what extent it is amplified. Once the basic foundation of the model was constructed (i.e. the five ‘Cs’ and the general process of disinformation), a second literature review of empirical studies was then conducted to identify, develop and explain the subfactors for each main factor. This was necessary because although the meta-analyses provided useful broad overviews of the phenomena of disinformation, a more granular approach was needed to better understand and describe the specific mechanisms of each sub-factor and why it is relevant to the overall model. For instance, under the main code Causes, the following subcodes were used: Creators and Motives. In turn, they could have their own subcodes as well. For example, under the subcode Motives was a lower level of codes: Political and Financial. Although a distinction was made between the use of overarching literature reviews and empirical studies in this process, there is some overlap between the two types of literature reviewed for this paper in explaining and supporting certain subfactors. This was done in order to simultaneously provide the necessary level of detail required to understand the specific mechanisms of disinformation in the subfactors, while also ensuring it is understood within the wider context of disinformation and its impact on society. Therefore, when explaining and justifying (sub-)factors in the main body of this paper, theoretical and empirical evidence is at times intertwined. By using this layering in the coding, a useful overview and step-by-step breakdown of the important elements of disinformation could be identified. Moreover, the model was refined by turning the analysis around; the authors zoomed out, undertook a high-level analysis of all the subcodes breakdown and were able to detect the overarching themes: the main C5 elements and subsequent factors could then be either re-affirmed or altered (Fig.1). Fig. 1 The coding process Arcle X Arcle X Arcle X Subcode (ex: channels) Subcode (ex: forms) Subcode ( ex: strategies) Code (ex: Content) Themac analysis refers to Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 The main codes correspond to so-called ‘C5 elements’ in the model, while the subcodes correspond to factors and subfactors. At times, it was necessary to rework multiple aspects of elements based on the authors’ own discretion, which itself was based on findings in the literature. The extent to which codes and subcodes were cross-referenced and their occurrence in the literature also influenced whether they were integrated into the model. As the figure above demonstrates, one article could be relevant for multiple (sub-)codes, as it can discuss different topics. On the other side, some initial subcodes were deleted because they were not reflected strongly enough in the literature. Finally, it should be noted that the list of factors is not exhaustive, and their descriptions are not all-encompassing because disinformation, along with the online information environment, is constantly evolving and highly dependent on contextual circumstances. This article focuses on the most important and thematically relevant factors and their interrelationships.7 3 The C5 interaction model The coding of relevant literature highlighted five main aspects of disinformation that constitute the C5 elements: Context, Causes, Content, Consequences, and the Cycle of Amplification. Although the role of the Cycle of Amplification has been identified in multiple recent articles, emphasising the importance of it to the development of disinformation is one of the main contributions of this model. This model (see Fig.2) outlines the interactions and interrelationships between these five factors and its subfactors. The model also seeks to emphasise that, due to the nature of the relationships between these factors, the same piece of content can have different consequences, depending on which factors are at play and to what extent. This is important when trying to develop an understanding of how disinformation campaigns can lead to harmful or even violent consequences. The C5 Interaction Model (shown in Fig.2) depicts the five elements that play a role when exposure to disinformation content leads to cognitive and behavioural effects: Fig. 2 The C5 interaction model 7 All initial, the way they were found and subsequently coded articles for the conception of the model were organised in an Excel sheet which will be made available upon request. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research 1. The Context (social, cultural, political, or economic factors, important events, or relevant trends). 2. The Causes (creators and their motives). 3. The Content (the tailored piece of disinformation). 4. The Consequences (short and long-term effects on the consumer and society). 5. The Cycle of Amplification (the interaction of receiver susceptibility, dissemination factors, and the possible interventions to counter the cycle). The following sections describe each of the main elements of the C5 Interaction Model, its factors and subfactors, in more detail. Each section starts with a visualisation to present an overview of the breakdown of each element. The corresponding factors and subfactors are only meant to display that they are grouped together in different layers: each element (C in the darkest colour), can be categorised in a layer of broad factors (slightly lighter colour), which in turn can be categorised into more detailed subfactors (lightest colour). For instance, looking into C1 Context could mean analysing ‘Setting’ which can be done by investigating the ‘region’ and/ or ‘period’. This list is indicative and non-exhaustive. It covers the main elements of disinformation. Moreover, many interrelations exist between the factors and subfactors. The most relevant ones are described in the following sections. 3.1 Context As previously discussed, disinformation is considered to be a ‘context-bound phenomenon’; which is to say that the context in which the disinformation exists is fundamental to understanding the actors, intentions, and techniques behind the manipulation [24]. Indeed, as argued by Zhang and Ghorbani [28], social context is a crucial determinant of the extent to which messages are disseminated, to what extent they are amplified and what effects they may have. Despite this, however, the importance of context is often overlooked in academic literature on disinformation [22]. Context refers to anything related to the social, cultural, political, or economic setting or environment, including important events or relevant trends, in the wider society(see Fig.3). C1 Context Setting Region Period System Societal trends Hybrid threats Political climate Media ecosystem Events Political Social Breaking news Fig. 3 A breakdown of the C1 element context Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 3.1.1 Setting This is perhaps the most important contextual factor that influences the potency of disinformation is the setting in which it is sent and received. The geographical region or period in which a disinformation campaign is waged will determine what the disinformation looks like, what it says, how it is presented, and who it targets. Setting, therefore, intrinsically influences the creation, dissemination, amplification, and effects of disinformation. The specific region in which the disinformation is disseminated is a fundamental subfactor to consider. As Humprecht [30] demonstrated, the content and style of disinformation changes from country to country. For example, online disinformation in the US and the United Kingdom (UK) is predominantly politically partisan, whereas in Germany and Austria sensationalist stories predominate over political content [30]. This can also be explained by the unique political systems in the US and the UK, discussed in more detail below for political system. Furthermore, Humprecht [30] found that online disinformation in English-speaking countries tends to target political actors, whereas in German-speaking countries, the main focus is immigrants. As such, other region-specific indicators are important to consider when assessing the contextual conditions of disinformation, such as how resilient a society is, levels of populism, polarisation, media trust, time spent on social media platforms, media literacy, and the strength of the public broadcasting service. Furthermore, the cultural context of a region can be a useful determinant of how effective disinformation will be, how susceptible people are and the extent to which it can spread (see Chapter3.1.5). Aside from regional differences, the specific period in time in which disinformation is disseminated is an important contextual subfactor. Overall, disinformation campaigns that are being waged today can be considered to take place in the so-called disinformation or ‘post-truth’ age [31] which refers to the present era of corrupt information environments [32]. Within this era, more contentious periods take place that feed disinformation, like the COVID-19 pandemic [7]. The WHO (World Health Organisation) even called the increase in disinformation due to COVID-19 an international ‘infodemic’. Since there was a lot of insecurity around COVID-19, which scientists could not provide an immediate response to, it was fertile ground to create and spread disinformation [33].8 In response, the WHO launched a special platform for policymakers, academics and public health professionals to come together with media organisations, social media platforms and civil society to devise a new framework on how to deal with the COVID-19 infodemic [34]. Perhaps the most important subfactor for setting is the political system. This broadly refers to the formal, constitutionally enshrined institutions, processes and structures that constitute a state and its political order [35]. This is distinct from the subfactor political climate which refers to more temporal political discourses at a particular time, rather than the more static and permanent regime of institutions and processes that comprise a political system.9 As comparative research shows, the type of political system (non-democratic versus democratic regimes) is a strong determinant of the likelihood of disinformation spread [36]. Such research on different types of democracies specifically is, unfortunately, scarce.10 Nevertheless, it should be acknowledged that, for instance, the rigid two-party system used in the US, and to a lesser extent in the UK, is unique compared to other advanced democracies in Europe which generally operate under a multi-party system. Similarly, the UK’s first-past-the-post electoral system is also unique compared to the proportional representation systems favoured by other European states. Therefore, the way in which disinformation manifests, spreads and takes effect may be vary depending on the particular type of democratic system. 3.1.2 Societal trends Similar to the setting, societal trends also play a significant role in determining the nature of disinformation. The concept of Societal Trends refers to a more specific, temporal set of trends in society that can influence how disinformation is created, disseminated, amplified, and takes effect. For instance, although EU countries may have a similar setting (region and period), each country—and within that, each social group or political class—will be facing different types of threats, be exposed to different political challenges, and be part of different media ecosystems, thereby influencing the nature of disinformation to which they are exposed. 8 More about the amplification process can be found in C5 3.1.5 Cycle of Amplification. 9 More about political climate can be found in C1 3.1.1.2.5. 10 However, considerable research has been conducted measuring phenomena such as polarisation in different political contexts, such as European multi-party systems (e.g. [132]). Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research The targets of disinformation are often unaware that such illegitimate uses of digital media are in fact related to wider hybrid threats and information warfare activities [25]. Hybrid threats refer to activities of an adversarial that are difficult to detect, attribute and hence to counteract and remain below the threshold of war. In that context disinformation campaigns are popular to sow discord amongst populations through for example proxies to make it difficult to see the true source. In recent years, foreign actors and their proxies have begun weaponising information to fulfil their nefarious motives and forcefully alter public beliefs or perceptions about a certain person, event, or body [37]. This is often referred C2 Causes Creators Organisation / Individual Non-human/ Human State / Non-state Motives Ideological Financial Fig. 4 A breakdown of the C2 element causes C3 Content Forms Text Headlines Visuals Channels Social media News websites Influencers Strategies Persuade Undermine Exploit Fig. 5 A breakdown of the C3 element content Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 or hate speech, does fall under the jurisdiction of law enforcement. Furthermore, these consequences can in turn lead to changes to the context in which the disinformation takes place, creating a feedback loop between Consequences and Context, as denoted in Fig.2. 3.4.1 Micro effects Micro-level effects relate to cognitive effects experienced at the individual-level as a result of exposure to disinformation, which could tap into shared grievances. In other words, the psychological impact of disinformation on an individuals’ cognition. As seen in Chapter3.1.3, the strategy of a disinformation campaigns is often to undermine populations, casting doubts and sowing division to allow for the potential of persuasion, whether that be shifts in attitude or behaviour. Considering that the scope of this paper is about disinformation that can lead to harmful or even criminal behaviours, the following section is written on that assumption that the initial process of casting doubts and undermining audiences has already taken place, therefore attitudes and behaviour change may then be possible to influence. It is important to caveat this with the fact that it is extremely difficult to ascertain causal links between disinformation and attitudinal and behaviour change; however, they remain important considerations when analysing possible consequences of disinformation. A fundamental goal of disinformation is persuasion. Research shows that disinformation, disseminated via online social networks in particular, is effective in affecting political attitude change in individuals [85]. The ways in which beliefs become established in individuals are numerous (identity, political knowledge etc.), however people often rely on cognitive shortcuts to form beliefs and make sense of the vast information environment [23]. For instance, an oft-cited explanation for the persuasive power of disinformation is confirmation bias: an individual’s tendency to react positively to information that matches their prior beliefs [23]. Another explanation of persuasiveness concerns the Elaboration Likelihood Model theory which posits that individuals process information through two paths: (1 a central path of argument evaluation that requires high cognition,and (2 a peripheral path that relies on heuristics and requires less cognition [86, 87]. The extent to which information is persuasive depends on which path the information is evaluated through. From persuasion comes conviction. This refers to “the incorporation of a given fake news narrative into an individual’s mental model as a deeply held belief” [23, 23]. Ultimately, one of the main desired effects of disinformation is often behaviour change, directly or indirectly. Recent research by Bastick [88] found that even limited exposure to disinformation (less than five minutes) was enough to alter individuals’ unconscious behaviour. In a political context, it has been shown that exposure to disinformation—particularly if it was politically charged and intentionally fabricated—had a positive effect on electoral support for populist parties, regardless of prior support [89]. Populist parties are not the only ones who engage in disinformation, although it does tend to stem mainly from parties on the extremities of the political spectrum [46, 90, 91]. Although disinformation alone cannot explain growths in populism, for example, there are signs that disinformation has the potential to influence unconscious behaviour change, which can therefore lead to changes in conscious behaviours such as voting and demonstrating. 3.4.2 Meso effects Individual-level effects can aggregate to generate group-level effects, otherwise known as ‘meso effects’. As disinformation spreads and pollutes an information environment, effects at the individual level will eventually impact groups and communities, potentially leading them to engage in rumour, conspiracy or even seek out extreme viewpoints. Because of the divisive nature of disinformation [92], people will tend to connect even more with their ingroup (‘their own’,people with similar beliefs and experiences) and widen the gap with the out-group (‘the other’). This is called affective polarisation [93]. Susceptible members of groups may reinforce and legitimise each other’s beliefs. Social dynamics can influence and strengthen in-group/out-group perception, which lies at the core of polarisation processes, can lead to organised extremism, and is common in disinformation campaigns. The group becomes a factor of influence on the cognitions and behaviours of its members, leading to a potential threat to individual autonomy and neutralising individual critical thinking [88]. Furthermore, the collective polarisation and radicalisation process provides psychological safety and further motivational triggers for individuals to mobilise and engage in collective behaviours, including civil unrest and potentially violence. It is well established that communication flows and information controls are “indispensable ingredients of violent conflict” [94], and there is growing evidence that false or conspiratorial information can be a catalyst for group violence. For example, research has shown that the spread of disinformation on WhatsApp in India could be a contributing factor Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research to an increase in lynchings and violence as users were being predisposed to hate a certain group and encouraged to engage in violence either motivated by prejudice or by rumour [95]. Furthermore, an analysis of data from more than 150 countries worldwide showed that the dissemination of disinformation is among the drivers of domestic terrorism [96]. That said, as mentioned previously, the causal links between disinformation and offline unrest or violence are difficult to grasp and there are countless mitigating factors that must be considered when assessing behaviour influencing and change. Collective behaviour can also mean inaction. Audiences exposed to a lot of disinformation may experience the phenomenon of “information learned helplessness” whereby people are so engulfed in false information that they simply give up trying to ascertain the truth [97]. 3.4.3 Macro effects Individual-level effects can aggregate to generate group-level and then societal-level effects. This is referred to as ‘macro effects’. As individual beliefs become entrenched and news consumption becomes more partisan, the prospect of polarisation in society becomes more likely. The mindset of ‘us vs. them’, often initiated at the individual-level through persuasion and conviction, is easily extrapolated to the societal-level once people form groups with other like-minded individuals based on a shared distinct belief [23]. Polarisation occurs when these groups position themselves in opposition to other belief groups, which creates division and cleavages. Polarisation most often relates to political ideology or social identity, but it can be predicated on numerous factors. The next step from polarisation is aversion, defined as “the complete repudiation of opposing views and those that hold them” [23]. Individuals who are strongly convicted in their beliefs and are members of polarised groups can quickly transition from benignly avoiding oppositional viewpoints to actively attacking those who hold opposing views, or supply information, contrary to their own [23]. Aversion therefore goes beyond mere polarisation (forming of partisan groups), and is the process by which these groups proactively antagonise and resist their opponents. Polarisation and aversion can lead to a climate of distrust in society, in which the divergence between oppositional groups causes an erosion of trust. At a societal level, distrust can negatively impact a multitude of factors, such as political institutions, the rule of law, the media, or society itself [98]. An example of this that relates to disinformation is the phenomenon of hostile media effects, discussed above, which is the culmination of polarisation and aversion to create widespread suspicion and distrust of mainstream news agencies [23]. These effects, whether at the micro or macro level, are the potential consequences of disinformation. As mentioned above, these consequences can in turn influence or change the nature of the context in which the disinformation takes place, thereby creating somewhat of a feedback loop between the Consequences and Context factors in the C5 Model. For instance, if a consequence of disinformation is that it influencers individual attitudes, collective action or levels of polarisation in society then this impacts the nature of the context, whether that is in regard to the setting, societal trends or even specific events that have been instigated as a result of the disinformation. 3.5 Cycle ofamplification The Cycle of Amplification refers to the relationship between dissemination, or propagation, and persuasion, which is usually the overarching goal behind disinformation(see Fig.7) [23]. As such, this element demonstrates the interaction between the first four elements: within a certain context (C1), disinformation messages are created (C2), and based on its content (C3), the susceptibility of its receivers, (C5) and the dissemination (C5), effects are created (C4), which in turn can become causes in itself for new disinformation messages or campaigns. To counter the effects of the cycle of amplification, practitioners can employ interventions (C5). This refers to any measure that is designed to mitigate or prevent the spread and impact of disinformation on society. Here, we are not concerned with (psychological or sociological) effects of disinformation on society but instead with potential interventions that are implemented to counter the dissemination of disinformation or (in)direct threats posed by disinformation. 3.5.1 Susceptibility It is important to note that susceptibility is not a set variable and can constantly evolve. This also means that it is difficult to establish exact profiles of individuals who would never be susceptible to disinformation, because it can change over time. Moreover, in general people get information through testimony and tend to believe information by testimony, Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 meaning that they only tend to doubt at a later stage [99]. Susceptibility comes from a combination of traits that are more permanent, like personality traits [100], but also from variations of mood, emotions, and state of mind. All individuals are, to some extent, led and affected by emotions which influence perception and reaction to the processed information [80]. Also, context plays a large role in affecting one’s psychology. For example, some contextual amplifiers may have made more people vulnerable to disinformation during the COVID-19 lockdowns such as the constant uncertainty or the overload of information [79]. In sum, not every individual will be triggered by the same piece of disinformation and if they are, they will not be triggered in the same way.16 One’s beliefs are ideological attachments, in other words, core thoughts that influence the rational and emotional information process of an individual [68]. Or, in other words: that which an individual believes to be true. In the case of disinformation, Galeotti [99] argues that beliefs influence the susceptibility of the individual to accept or reject a piece of new information, as they will tend to accept ideas that are aligned with existing beliefs. This can mean that disinformation about something that previously was not particularly important to a person (e.g., how vaccines were made in the context of COVID-19), suddenly generates traction online. Research indicates that a low trust in established governmental and media institutions turns consumers to alternative media sources and makes them more susceptible to disinformation [101]. Galeotti [99] also highlights that the more conservative the beliefs, the more the individual may believe in disinformation. Lastly, reinforced extreme beliefs tend to influence polarisation and binary thinking (false vs true, wrong vs right). Another factor of influence for susceptibility is found in heuristics and biases [86, 102]. The human brain constantly makes a high number of quick decisions, most of them unconscious. To reduce the cognitive effort, the brain uses heuristics, or shortcuts, to make quick, deeply rooted, and almost automated decisions [103]. When people are confronted with a piece of information, they are generally biased to believe in its validity, which is referred to as the truth bias [104] and is similar to the testimony belief referenced to earlier [99]. Heuristics and biases are mostly influenced by beliefs, which they also reinforce. An example of this are the earlier discussed confirmation bias and the Elaboration Likelihood Model theory.17 The strength of the confirmation bias is also closely related to contextual factors and individual characteristics [105]. Even a single exposure to disinformation can increase the perception of its accuracy. This is also referred to as the ‘illusory truth effect’ and can pertain even though it is flagged as disinformation at a later stage [106]. Exposure to disinformation in itself is not a determinant of susceptibility, but when it occurs it strengthens the effects of one’s beliefs and their biases. By increased exposure, beliefs, polarisation, or even radicalisation can be reinforced. Similarly, one’s relationship to its social network, and most often the need to belong can increase the susceptibility of gossip consumption and engagement and therefore exposure and the risk of consuming disinformation [107]. 3.5.2 Dissemination Dissemination or propagation refers to the distribution of disinformation [23]. This is enabled by online social networks since they make it easier for users to share disinformation. High virality metrics, including numbers of likes or shares, and group norms further amplify the dissemination [23]. This factor is closely related to the context as well. For example, growing distrust in media—as a contextual feature of the political climate in a given environment—is seen as an important influencer of the consumption and dissemination of disinformation [108]. Several authors confirm that people’s social status is strongly related to their propensity to share news content [109–111]. Specifically, the user can feel that their social reputation is reinforced by showing their social network (which can exist of several groups) that they are up to date on the latest news. New and impressive information, which is mostly consistent with the main characteristics of disinformation, can lead to greater group acceptance [99]. Certain individual characteristics influence to what extent someone is vulnerable to not only consuming disinformation but sharing it as well. Guess etal. [100] examined which individual-level characteristics were relevant with regards to sharing disinformation on Facebook during the 2016 US presidential campaign. After controlling for other demographic characteristics, the authors found that political affiliation and age significantly influenced who shared disinformation. Conservatives and older people were found to share more [100]. Various other studies replicated the finding that 16 In addition, individual characteristics are discussed under 3.1.5.2 Dissemination and contextual factors are analysed under 3.1.1 Context. 17 See 3.1.3.1 Micro effects. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research ideologically and politically speaking, right-wing oriented people are more likely to consume and spread disinformation [68, 101, 112–114]. Other individual characteristics that influence disinformation consumption are being widely challenged in academia, including age, gender, and internet usage [68]. Echo chambers are created when members of a community share disinformation with each other in specific (online) groups, which leads to other members reading and sharing that same message as well. This phenomenon contributes significantly to the spread of disinformation online [70, 115]. Rini [116] disinformation reaches a user through the testimony of another person, who shared it after accepting it as being true. The testimony is transmitted, especially on social media, often in a biased way, since it comes from someone who has just shared an ideology or expressed a party attachment. This information will be accepted and shared by a recipient who agrees with these social values [116]. Moreover, the spreaders in this echo chamber might unintentionally contribute to the spread of disinformation, since they might just be eager to participate in their (online) community [28]. Disinformation has found increased exposure on Facebook and Twitter (now X) due to the segregation of groups and highly advanced recommendation algorithms that are now key features of online social networks—although users themselves still play a crucial role in the creation of so-called ‘filter bubbles’ or ‘echo chambers’ [70]. 3.5.3 Interventions Interventions refer to efforts to mitigate the amount, spread, or impact of disinformation. This includes the disruption of the amplification cycle [23]. Disinformation detection is the task of assessing the truthfulness of a certain piece of news. This can be done manually by fact-checking experts but can also be done through automated analyses using data-mining and/or machine learning, although these techniques are in their infancy [28]. Fact-checking forms part of the detection process by verifying the information contained in an alleged piece of disinformation which, again, can be done manually or through automated means [117]. The fact-check is then usually advertised by means of flagging, which simply alerts people to information falsehoods and stimulates critical thinking, as well as lowering the likelihood of users’ “intentions to share the article” [25]. A specific type of flagging is source rating, which involves the provision of additional information online related to the sources of information contained in supposed disinformation. Source ratings influence the believability of articles, which makes readers more sceptical of news stories on social media platforms and online news sites regardless of the source’s credibility, while low source ratings lowered the believability of the article and reduced reader engagement [118]. Going beyond mere detection, a valuable intervention in response to disinformation is debunking. This is the process whereby disinformation is detected, flagged, and the false information contained in disinformation is corrected or rebutted [25]. Research shows that debunking disinformation by providing a rebuttal and introducing corrective information is far more effective than merely labelling the article as disinformation [119]. However, in general, the effects of debunking can vary considerably and depend on many factors, including the level of detail included in the debunking information, the level of reasoning behind people’s belief in the information, and the time between the disinformation publication and the rebuttal [119, 120]. AI can also be used in debunking efforts [121]. A standout example of debunking in action is the ‘Debunk EU’ initiative which incorporates AI tools, volunteers and journalists to research and debunk disinformation in the Baltic States and beyond [122]. A more proactive countermeasure to disinformation is pre-bunking, sometimes referred to as inoculation. The goal is to build societal resilience against the dangers of disinformation, in order to pre-empt its effects. If people are educated about the threat of disinformation—for example, through increased media literacy [123]—and forewarned that they may be targeted, they will become immunised against disinformation [124, 125]. This also depends on various other key indicators of societal resilience-building, such as levels of populism, polarisation, media trust, time spent on social media platforms and the strength of the public broadcasting service [126]. It proves that interventions can also influence the context eventually. Furthermore, generating social norms around disinformation reporting and detection can lead to higher rates of such reporting by individuals [127]. Another intervention for disinformation is greater regulation in online environments. As the threat of disinformation has grown in recent years, so too have calls for more regulation. However, this is a controversial intervention that is fraught with challenges and has sparked considerable debate as policymakers rankle with developing regulatory frameworks at national or regional levels [128, 129]. The EU’s Strengthened Code of Practice on Disinformation 2022 compels signatories to: demonetise the dissemination of disinformation; guarantee transparency of political advertising; improve cooperation with fact-checkers; and facilitate greater access to data for researchers [130]. This comes as part of greater regulation of disinformation by the EU via the Digital Markets Act 2022, the Digital Services Act 2022, and the forthcoming Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Artificial Intelligence Act. Such regulation is important as it dictates the legal and normative boundaries within which online social networks, for example, can operate and the extent to which they are required to curate their content—the greater regulation, the more online social networks will be required to curate their content to remove disinformation. 4 Discussion Recent examples of social unrest around the world, like the storming of Capitol Hill in the US in 2022, have illustrated how disinformation can fuel division and incite violence. It is therefore vital for authorities tasked with dealing with the real-world implications of disinformation to understand the process of how it is created, disseminated, and takes effect in society. The present article sought to outline the disinformation process, from creation to effects, to illustrate the process by which the spread of disinformation can escalate to harmful or even criminal behaviours. By synthesising academic literature on disinformation from various fields or research, the aim was to provide practitioners with an accessible and practical guidance on the dynamics of disinformation that strikes a balance between theory and practice. The C5 Model outlines the interactions and interrelationships between the five main factors and its various subfactors. This model can be used in a versatile way by different users. Moreover, the C5 Model contributes to a gap in the academic literature by combining a diverse set of insights on disinformation into one cohesive framework. Another novel feature of this model is that it emphasises that, due to the nature of the relationships between these factors, the same piece of content can have different consequences, depending on the context. The following sections elaborate on both the practical and academic contribution of this article. 4.1 Practical contribution Based on interviews with five European PAs, a shared need was identified to improve countermeasures against disinformation that can lead to criminal behaviours.18 According to the PAs, the first step to improving their counter-disinformation capabilities is to develop an in-depth understanding of the phenomenon. None of the PAs that the authors consulted have a dedicated team at their disposal to understand, prevent and counter the harmful and potentially criminal consequences of disinformation. Some PA departments might have one dedicated officer, but it is the norm that these duties are conducted alongside other unrelated police duties. By developing a practical framework that breaks down the complex concept of disinformation into constituent elements, this research will help facilitate a deeper understanding of the phenomenon. It encourages systematic thinking prompting not only the interviewed PAs, but practitioners in general, to consider all stages of the disinformation lifecycle rather than focusing on isolated aspects, and understand how these aspects fit into the broader picture of a campaign. Practically, the C5 Interaction Model has been developed specifically for the use of practitioners throughout the different phases of the counter disinformation process. First, it can be used preventively. The model can be employed as course material to teach fellow colleagues about the workings of the disinformation lifecycle. This way, more officers will be competent in spotting and acting on potentially harmful disinformation messages. Moreover, an understanding might be created about recurring events (nationally or internationally) that inspire disinformation (e.g. elections or events revolving around polarising topics in that specific society). This can enable practitioners to anticipate disinformation and mitigate its effects on society. Second, the C5 Interaction Model can be used on an ongoing basis as a decision-support system. If a piece of disinformation is spotted, the model might help the officer to think of important elements that are related to that specific message and therefore aid in deciding whether it would be worthwhile to further investigate. Third, the C5 Interaction Model can be used retrospectively to analyse the disinformation process after events have already occurred. This can help with post hoc analysis of specific situations to improve future readiness and responsiveness. 4.1.1 Use case summary To exemplify the efficacy of the C5 Interaction Model, it was applied to a use case from Catalonian authorities. The application illustrates how the model can be used to identify and understand the various processes of a specific real-world case of disinformation that led to harmful or criminal behaviour(see Fig.8). 18 These interviews were carried out within the context of the VIGILANT project. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research The Catalonian use case concerns a public Telegram channel which spreads hate narratives linked to far-right ideologies among approximately 1200 followers. The administrators of the Telegram group used disinformation as a tool with the aim of polarising the receivers and generating a climate of hatred towards various groups. The figure above summarises how the model was applied to understand the Catalonian case.19 The breakdown of the elements in the illustration shows that the Catalonian disinformation campaign took place in a context of political tensions. The creators used the social power of the community and its social network, contained in the Telegram channel. The political context was probably the catalyst for like-minded people to join the Telegram channel in the first place, thus creating a favourable environment for the disinformation to be received, and most likely spread even outside of the group, thus recruiting more members. The content, which aligned with the members’ beliefs, was most likely a trigger to further radicalisation of the group. 4.2 Academic contribution The field of disinformation research is expanding rapidly, with insights emerging from disciplines such as communication studies, psychology, political science, sociology, and computer science. However, this exponential growth also carries the risk of fragmentation, as researchers often focus on isolated aspects of the problem. The C5 Interaction Model represents one of the first concerted efforts to bring these diverse insights together into one comprehensive, integrative framework. While previous frameworks in academic literature have explained aspects of the disinformation spread (e.g. [19]), sustainment of effects (e.g. [21]), and amplification of messaging (e.g. [23]), the present article has sought to connect all these factors into one comprehensive framework, while simultaneously demonstrating the interrelationships and fluid interconnectedness of the entire process of disinformation creation, spread, and effects. As far as known, no similar comprehensive framework has been developed yet. The C5 Interaction Model shows that there is not one fixed route that a piece of disinformation takes, making it a highly complex phenomenon to research. Moreover, the route is predominantly influenced by the social context in which it takes place, a factor somewhat neglected in prior literature [22]. This provides a more nuanced appreciation of the underlying mechanisms of disinformation which goes beyond conventional models of communication that denote a linear relationship between sender and receiver (e.g., [131]). The C5 Model puts together disparate pieces of the enormous puzzle of disinformation research. An example of this is the incorporation of the Cycle of Amplification, a recent addition to the body of disinformation literature [23], as a main factor in the model explaining how the impact of disinformation is mitigated by the amplification cycle. Furthermore, as highlighted in the literature review, there is a lack of work that explores the links between disinformation and potentially harmful or criminal behaviour, such as social unrest, public disorder and hate speech, in a comprehensive and practical manner. 4.3 Limitations andfuture research It should be noted that the C5 Interaction Model is not exhaustive. The online environment where disinformation spreads, which also interacts with the physical world, is constantly changing. Therefore, the factors outlined in the C5 Interaction Model should be thought of as thematic labels under which numerous specific examples can be categorised, rather than rigid, definitive concepts. Furthermore, the categorisations and descriptions of the (sub-)factors are based on the authors’ current understanding of the disinformation phenomenon, which is ever-changing and growing. As such, continuous research is needed to ensure the conceptual model remains relevant and accurate. Similarly, further research is required to validate the findings of this article. Steps have already been taken by the authors to test the model, by way of use cases to support its conceptual validity and through workshops with practitioners to ensure its practical relevance. However, more rigorous validation of the model, both in terms of theoretical validity and practical implementation, is required to solidify the findings of this article. A suggestion would be to build on the application of the C5 Interaction Model to the Catalonian use case. More of such examples of practical applications are needed to analyse in which ways the model can fit into current practises and processes of practitioners. However, this would just validate the model’s retrospective appliance, whereas—as discussed in the practical contribution section—the model could also be used preventively and on an on-going basis. To test the preventive value of the model, practitioners are encouraged to start using it for educational purposes. These sessions could be observed and analysed by researchers 19 A full summary of the Catalonian case study can be made available by the authors on request. Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 and the respective authorities. The same goes for using the model as a decision-support system. Questions that would focus on the practicality, understandability and applicability of the model could guide such evaluations. Also, a vital dilemma remains: when does disinformation become dangerous enough for practitioners to act upon? Future research could analyse this question, whereby it is important to take into account individual societies legal and ethical frameworks. Another potential critique of this paper is the use of both theoretical and empirical studies in the development of the C5 Interaction Model. As stated in the methodology section, the foundations of the models—in other words, the five main ‘Cs’ and their ordering in the process of disinformation—were developed using primarily meta-analyses and existing literature reviews. This allowed for a broader analysis of the general disinformation processes. When developing the subfactors, empirical studies were relied upon more. As aforementioned, despite this distinction there remains some overlap between these two types of literature and the weight given to each type of source is not clear. However, this was done in order to simultaneously provide the necessary level of detail required to understand the mechanisms of disinformation, while also ensuring it is understood within the wider context of disinformation and its impact on society. Given the intended audience of this paper and the emphasis on the real-world application of its findings, the need for clarity and efficacy in presenting the most valuable information to practitioners superseded any concerns, albeit valid, over the blurry distinction between theoretical and empirical evidence. Also, the reliance on evidence from mainly two-party political systems might be seen as a limitation, because this paper is focused on European practitioners who operate in multi-party political systems. However, much of the literature on the phenomena of disinformation is centred around the US or the UK—two distinctively unique political systems that are not wholly generalisable to a European context. This is likely due to the recent high-profile bipartisan political and constitutional events that were cornerstones in manifesting global attention and research on disinformation and its effects, namely the political rise of Donald Trump and the Brexit referendum. Any bias towards US and UK sources, research and case studies is somewhat inherent to disinformation research because these events garnered such international attention and provided such fertile ground for analysis. Furthermore, comparative research on disinformation in different political systems has tended to focus on democratic versus non-democratic regimes rather than differences within democracies, such as two-party or multi-party political systems. As such, this is an obvious issue for which further study is required to understand better how disinformation manifests, spreads and takes hold in varying types of political systems. Nevertheless, this paper endeavoured to provide a more European outlook, utilising a case study from Catalonia at the main case to exemplify the use of the C5 Interaction Model, while also using research from other parts of the EU, such as Portugal, Germany, the Netherlands and Italy. 5 Conclusion The main contribution of this article is the C5 Interaction Model, which represents one of the first concerted efforts to bring multidisciplinary insights on disinformation together into one comprehensive integrative framework. The model, consisting of five key interrelated elements of disinformation (Context, Causes, Content, Consequences, and Cycle of Amplification), can increase understanding on the dynamics and consequences of the disinformation lifecycle by providing a high-level overview of this complex process. Based on a synthesis of academic theory and literature, the C5 Interaction Model also provides accessible and practicable guidance to practitioners tasked with handling the real-world consequences of disinformation. Author contributions K.K., NB.F, B.C and S.vd.M. (all authors) wrote the main manuscript text. K.K., NB.F. and B.C. designed the figures. All authors reviewed the manuscript. Funding The initial research leading to these results received funding from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No: 101073921. Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research Declarations Ethics approval and consent to participate The Ethics work package from the VIGILANT project, led by the University of Freiburg, has approved the interviews with PAs. Consent to Participate and Consent to Publish were obtained from all participants in the study. Competing interests The authors declare no competing interests. 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To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ byncnd/4. 0/. References 1. Jamalzadeh S, Mettenbrink L, Barker K, González AD, Radhakrishnan S, Johansson J, Bessarabova E. Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks. Reliab Eng Syst Saf. 2024;243:1–11. 2. Rathje J. Driven by conspiracies: the justification of violence among “Reichsbürger” and other conspiracy-ideological sovereignists in contemporary dutchy. Perspect Terror. 2022;16(6):49–61. 3. European Commission. A multi-dimensional approach to disinformation: article of the independent High Level Group on fake news and online disinformation. Directorate-General for Communication Networks, Content and Technology. 2018. https:// digit alstrat egy. ec. europa. eu/ en/ libra ry/ finalreporthighlevelexpertgroupfakenewsandonlinedisin forma tion. Accessed 7 June 2023. 4. Stahl BC. On the difference or equality of information, misinformation, and disinformation: a critical research perspective. Inform Sci. 2006;9:83–96. 5. DiMaggio AR. Conspiracy theories and the manufacture of dissent: QAnon, the ‘Big Lie’, COVID-19, and the rise of rightwing propaganda. Crit Sociol. 2022;48(6):1025–48. 6. Woodruff Swan B, Lippman D. New Capitol Police document shows how unprepared they were for Jan. 6 riots. Politico. 2021. https:// www. polit ico. com/ news/ 2021/ 10/ 29/ capit olpolicedocum entsunpre paredjan-6riots517478. Accessed 5 June 2023. 7. Hollywood JS, Harrison B, Matthews M, Donohue RH. Police officers: this article will make you better at combatting misinformation. RAND Commentary. 2020. https:// www. rand. or g/ pubs/ c o mme n tary/ 2020/ 08/ howtocombatcovid19misin forma tion. h tml. Accessed 28 Oct 2024. 8. Zamparutti T, Jones M, Tugran T, Vona L, Navas L, Sidło K, Chmiel O. Developing a handbook on good practice in countering disinformation at local and regional level. European Committee of the Regions; Commission for Citizenship, Governance, Institutional and External Affairs. 2022. https:// doi. org/ 10. 2863/ 066582. 9. Arcos R, Chiru I, Ivan C, editors. Routledge handbook of disinformation and national security. New York: Routledge; 2024. 10. Kapantai E, Christopoulou A, Berberidis C, Peristeras V. A systematic literature review on disinformation: toward a unified taxonomical framework. New Media Soc. 2021;23(5):1301–26. 11. Alam F, Cresci S, Chakraborty T, Silvestri F, Dimitrov D, Martino GDS, Nakov P. A survey on multimodal disinformation detection.arXiv [Preprint]. arXiv: 2103. 12541. 2021. 12. Singhal S, Shah RR, Chakraborty T, Kumaraguru P, Satoh SI. Spotfake: a multi-modal framework for fake news detection. In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM). IEEE; 2019. pp. 39–47. 13. Zhou X, Jain A, Phoha VV, Zafarani R. Fake news early detection: a theory-driven model. Digit Threat Res Pract. 2020;1(2):1–25. 14. Fu, D., Ban, Y., Tong, H., Maciejewski, R., & He, J. (2022). DISCO: Comprehensive and explainable disinformation detection. In Proceedings of the 31st ACM international conference on information and knowledge management (CIKM 2022) (pp. 4848–4852). https:// doi. org/ 10. 1145/ 35118 08. 35572 02. 15. Santos FCC. Artificial intelligence in automated detection of disinformation: a thematic analysis. Journal Media. 2023;4(2):679–87. 16. DISARM Foundation. DISARM Framework. 2024. https:// www. disarm. found ation/ frame work. Accessed 28 Oct 2024. 17. Terp SJ, Breuer P. Disarm: a framework for analysis of disinformation campaigns. In: 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). IEEE; 2022. pp. 1–8. 18. Kozyreva A, Lorenz-Spreen P, Herzog SM, Ecker UK, Lewandowsky S, Hertwig R, Wineburg S. Toolbox of individual-level interventions against online misinformation. Nat Hum Behav. 2024. https:// doi. org/ 10. 1038/ s4156202401881-0. 19. Pennycook G, Rand DG. The psychology of fake news. Trends Cogn Sci. 2021;25(5):388–402. 20. Rabb N, Cowen L, de Ruiter JP, Scheutz M. Cognitive cascades: how to model (and potentially counter) the spread of fake news. PLoS ONE. 2022;17(1): e0261811. 21. Froehlich TJ. The role of pseudo-cognitive authorities and self-deception in the dissemination of fake news. Open Inform Sci. 2019;3(1):115–36. 22. Arayankalam J, Krishnan S. The spread and impact of fake news on social media: a systematic literature review and future research agenda. E-Serv J. 2022;14(1):32–95. 23. George J, Gerhart N, Torres R. Uncovering the truth about fake news: a research model grounded in multi-disciplinary literature. J Manag Inf Syst. 2021;38(4):1067–94. Vol:.(1234567890) Research Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 24. Hameleers M. Disinformation as a context-bound phenomenon: toward a conceptual clarification integrating actors, intentions and techniques of creation and dissemination. Commun Theory. 2023;33(1):1–10. 25. Arcos R, Gertrudix M, Arribas C, Cardarilli M. Responses to digital disinformation as part of hybrid threats: a systematic review on the effects of disinformation and the effectiveness of fact-checking/debunking. Open Res Europe. 2022;2(8):1–19. 26. Wolfswinkel JF, Furtmueller E, Wilderom CPE. Using grounded theory as a method for rigorously reviewing literature. Eur J Inf Syst. 2013;22(1):45–55. 27. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exercise Health. 2019;11(4):589–97. 28. Zhang X, Ghorbani AA. An overview of online fake news: characterization, detection, and discussion. Inf Process Manag. 2020;57(2):1–26. 29. Fathaigh RÓ, Helberger N, Appelman N. The perils of legally defining disinformation. Internet Policy Rev. 2021;10(4):1–25. 30. Humprecht E. Where ‘fake news’ flourishes: a comparison across four Western democracies. Inf Commun Soc. 2019;22(12):1973–88. 31. Tsfati Y, Boomgaarden HG, Strömbäck J, Vliegenthart R, Damstra A, Lindgren E. Causes and consequences of mainstream media dissemination of fake news: literature review and synthesis. Ann Int Commun Assoc. 2020;44(2):157–73. 32. Carmi E, Yates SJ, Lockley E, Pawluczuk A. Data citizenship: rethinking data literacy in the age of disinformation, misinformation, and malinformation. Internet Policy Rev. 2020;9(2):1–22. 33. Sánchez del Vas R, Tuñón NJ. Disinformation on the COVID-19 pandemic and the Russia-Ukraine War: two sides of the same coin? Humanit Soc Sci Commun. 2024;11(851):1–14. 34. Tangcharoensathien V, Calleja N, Nguyen T, Purnat T, D’Agostino M, Garcia-Saiso S, Briand S. Framework for managing the COVID-19 infodemic: methods and results of an online, crowdsourced WHO technical consultation. J Med Internet Res. 2020;22(6): e19659. 35. Heslop DA. Political system. Encyclopedia Britannica. 2025. https:// www. brita nnica. com/ topic/ polit icalsystem. Accessed 9 Apr 2025. 36. Hunter LY. Regime characteristics and online government disinformation. J Inform Technol Politics. 2025;1–20. 37. Meel P, Vishwakarma DK. Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-arts, challenges and opportunities. Expert Syst Appl. 2020;153: 112986. 38. European External Action Service (EEAS). Article on Foreign Information Manipulation and Interferences threats: TOWARDS a framework for networked defence. Strategic Communications, Task Forces and Information Analysis (STRAT.2). 2023. https:// www. eeas. europa. eu/ eeas/ 1steeasreportforei gninfor mationmanip ulati onandinter feren cethrea ts_ en. Accessed 28 June 2023. 39. Cardoso J, Narciso I, Moreno G, Palma N. Online disinformation during Portugal’s 2019 elections. ISCTE-IUL, MediaLab CIES-IUL. 2019. https:// democ racyrepor ting. org/ en/ office/ EU/ publi catio ns/ reportdisin forma tionduringportu gals2019elect ions. Accessed 28 June 2023. 40. Cardoso G, Moreno J, Narciso I, Palma N. Social media disinformation in the pre-electoral period in Portugal. CIES e-Working Paper (No. 230/2020). 2020. https:// repos itorio. iscteiul. pt/ handle/ 10071/ 20667. Accessed 28 June 2023. 41. Weikmann T, Lecheler S. Visual disinformation in a digital age: A literature synthesis and research agenda. New Media & Society. 2022. https:// doi. org/ 10. 1177/ 14614 44822 11416 48. 42. Bokša M. Russian Information Warfare in Central and Eastern Europe: strategies, impact, countermeasures. The German Marshall Fund of the United States (GMF); 2019. 43. Hameleers M, de Vreese C. Perceived mis-and disinformation in a post-factual information setting: a conceptualisation and evidence from ten European countries. In: Tumber H, Waisbord S, editors. The Routledge companion to media disinformation and populism. Milton Park: Routledge; 2021. p. 366–75. 44. Vallone RP, Ross L, Lepper MR. The hostile media phenomenon: biased perception and perceptions of media bias in coverage of the Beirut massacre. J Pers Soc Psychol. 1985;49(3):577–85. 45. Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018;359(6380):1146–51. 46. Allcott H, Gentzkow M. Social media and fake news in the 2016 election. J Econ Perspect. 2017;31(2):211–36. 47. Howard PN, Kollanyi B. Bots, #StrongerIn, and #Brexit: computational propaganda during the UK-EU referendum.arXiv [Preprint]. 2016. arXiv: 1606. 06356. 48. European Policy Centre. Disinformation ahead of the EU Parliamentary Elections: a snapshot from Bulgaria, Germany, and Italy. 2023. https:// www. epc. eu/ conte nt/ PDF/ 2023/ Disin forma tion_ DP_-_ Eiw_ and_ EMD. pdf. Accessed 9 Apr 2025. 49. La Cour C. Theorising digital disinformation in international relations. Int Politics. 2020;57(4):704–23. 50. Bojovic J. The Brussels conspiracy: narratives of EU-related conspiracy theories in pro-Kremlin media. In Europe: Continent of conspiracies. Routledge; 2021. pp. 214–30. 51. Yablokov I. Conspiracy theories as a Russian public diplomacy tool: the case of Russia Today (RT). Politics. 2015;35(3–4):301–15. 52. Yablokov I. Russian disinformation finds fertile ground in the West. Nat Hum Behav. 2022;6(6):766–7. 53. Reuters. Russian ‘disinformation’ hyped Paris bedbug scare, French minister says. Reuters. 2024. https:// www. reut e rs. com/ world/ europe/ russi andisin forma tionhypedparisbedbugscarefrenchminis tersays20240301/. Accessed 30 Oct 2024. 54. Rietjens S. Unraveling disinformation: the case of Malaysia Airlines flight MH17. Int J Intell Secur Public Aff. 2019;21(3):195–218. 55. Ferrara E, Varol O, Davis C, Menczer F, Flammini A. The rise of social bots. Commun ACM. 2016;59(7):96–104. 56. Budak, C. (2019). What happened? The spread of fake news publisher content during the 2016 U.S. presidential election. In Proceedings of the 2019 World Wide Web Conference (pp. 139–150). https:// doi. org/ 10. 1145/ 33085 58. 33137 21. 57. Lazer DMJ, Baum MA, Benkler Y, Berinsky AJ, Greenhill KM, Menczer F, Rothschild D. The science of fake news: addressing fake news requires a multidisciplinary effort. Science. 2018;359(6380):1094–6. 58. Maftei A, Holman AC, Merlici IA. Using fake news as means of cyber-bullying: the link with compulsive internet use and online moral disengagement. Comput Hum Behav. 2022;127: 107032. 59. Rezayi S, Balakrishnan V, Arabnia S, Arabnia HR. Fake news and cyberbullying in the modern era. In: International Conference on Computational Science and Computational Intelligence, 2018 (CSCI). IEEE. 2018. pp. 7–12. https:// doi. org/ 10. 1109/ CSCI4 6756. 2018. 00010. 60. Hughes HC, Waismel-Manor I. The Macedonian fake news industry and the 2016 US election. PS Political Sci Politics. 2021;54(1):19–23. 61. Petratos PN. Misinformation, disinformation, and fake news: cyber risks to business. Bus Horiz. 2021;64(6):763–74. 62. Apuke OD, Omar B. Fake news proliferation in Nigeria: consequences, motivations, and prevention through awareness strategies. Humanit Soc Sci Rev. 2020;8(2):318–27. Vol.:(0123456789) Discover Global Society (2025) 3:58 | https://doi.org/10.1007/s44282-025-00194-5 Research 63. Shoaib MR, Wang Z, Ahvanooey MT, Zhao J. Deepfakes, misinformation, and disinformation in the era of frontier AI, generative AI, and large AI models. In: 2023 International Conference on Computer and Applications (ICCA). IEEE; 2023. pp. 1–7. 64. Horne BD, Adali S. This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. In: Paper presented at the Eleventh International AAAI Conference on Web and Social Media, Montreal, QC, Canada, May 15–18. 2017. 65. Munger K, Luca M, Nagler J, Tucker J. The (null) effects of clickbait headlines on polarization, trust, and learning. Public Opin Q. 2020;84(1):49–73. 66. Hameleers M, Powell TE, Van Der Meer TGLA, Bos L. A picture paints a thousand lies? The effects and mechanisms of multimodal disinformation and rebuttals disseminated via social media. Polit Commun. 2020;37(2):281–301. 67. Vaccari C, Chadwick A. Deepfakes and disinformation: exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Soc Med + Soc. 2020;6(1):2056305120903408. 68. Gupta M, Dennehy D, Parra CM, Mäntymäki M, Dwivedi YK. Fake news believability: the effects of political beliefs and espoused cultural values. Inform Manag. 2023;60(2):1–12. 69. Zarouali B, Dobber T, De Pauw G, de Vreese C. Using a personality-profiling algorithm to investigate political microtargeting: assessing the persuasion effects of personality-tailored ads on social media. Commun Res. 2022;49(8):1066–91. 70. Zimmer F, Scheibe K, Stock M, Stock WG. Fake news in social media: bad algorithms or biased users? J Inform Sci Theor Pract. 2019;7(1):40–53. 71. Bernal P. Fakebook: why Facebook makes the fake news problem inevitable. North Irel Leg Q. 2018;69(4):513–30. 72. Khurana, P., & Kumar, D. (2018). SIR model for fake news spreading through WhatsApp. Paper presented at the 3rd international conference on Internet of Things and Connected Technologies (ICIoTCT), Jaipur, India, March 26–27, 2018. 73. Gragnani, J. (2018). Um Brasil dividido e movido a notícias falsas: Uma semana dentro de 272 grupos políticos no WhatsApp. BBC News. https:// www. bbc. com/ portu guese/ brasil45666 742. 74. US Department of State. Alerting the world to RT’s global covert activities. Office of the Spokesperson of the US Department of State, Fact Sheet. 2024. https:// www. state. gov/ alert ingtheworldtortsglobalcovertactiv ities/. Accessed 30 Oct 2024. 75. Posetti J, Matthews A. A short guide to the history of ‘fake news’ and disinformation. Int Center Journal. 2018;49(3):577. 76. Van der Linden S, Roozenbeek J. Psychological innoculation against fake news. In: Greifender R, Jaffé ME, Newman EJ, Schwarz N, editors. The psychology of fake news: accepting, sharing, and correcting misinformation. Milton Park: Routledge; 2021. p. 147–70. 77. CNN. Survey: most Filipinos see fake news as a problem. CNN Philippines. 2022. http:// www. cnnph ilipp ines. com/ news/ 2022/ 10/ 11/ pulseasiasurveyfakenews. html. Accessed 13 July 2023. 78. Ong JC, Cabañes JVA. Architects of networked disinformation: behind the scenes of troll accounts and fake news production in the Philippines. University of Massachusetts Amherst, Communication Department Faculty Publication Series. 2018. p. 74. https:// schol arwor ks. umass. edu/ commu nicat ion_ facul ty_ pubs/ 74/. Accessed 23 July 2023. 79. Hoyle A, Powell T, Cadet B, van de Kuijt J. Web of lies: mapping the narratives, effects, and amplifiers of Russian COVID-19 disinformation. In: Gill R, Goolsby R, editors. COVID-19 disinformation: a multi-national, whole of society perspective. Berlin: Springer International Publishing; 2022. p. 113–41. 80. Martel C, Pennycook G, Rand DG. Reliance on emotion promotes belief in fake news. Cognit Res Princ Implic. 2020;5:1–20. 81. Valenzuela S, Piña M, Ramírez J. Behavioral effects of framing on social media users: how conflict, economic, human interest, and morality frames drive news sharing. J Commun. 2017;67(5):803–26. 82. Harber KD, Cohen DJ. The emotional broadcaster theory of social sharing. J Lang Soc Psychol. 2005;24(4):382–400. 83. Berger J, Milkman KL. What makes online content viral? J Mark Res. 2012;49(2):192–205. 84. Heath C. Do people prefer to pass along good or bad news? Valence and relevance of news as predictors of transmission propensity. Org Behav Hum Decis Process. 1996;68(1):79–94. 85. Gil de Zúñiga H, González-González P, Goyanes M. Pathways to political persuasion: linking online, social media, and fake news with political attitude change through political discussion. Am Behav Sci. 2021. https:// doi. org/ 10. 1177/ 00027 64222 11182 72. 86. Chen CY, Kearney M, Chang SL. Comparative approaches to mis/disinformation: belief in or identification of false news according to the elaboration likelihood model. Int J Commun. 2021;15(1):1263–85. 87. Petty RE, Cacioppo JT. The elaboration likelihood model of persuasion. New York: Springer; 1986. p. 1–24. 88. Bastick Z. Would you notice if fake news changed your behavior? An experiment on the unconscious effects of disinformation. Comput Hum Behav. 2021;116: 106633. 89. Cantarella M, Fraccaroli N, Volpe R. Does fake news affect voting behaviour? Res Policy. 2023;52(1): 104628. 90. Bennett WL, Livingston S. The disinformation order: disruptive communication and the decline of democratic institutions. Eur J Commun. 2018;33(2):122–39. 91. Farhall K, Carson A, Wright S, Gibbons A, Lukamto W. Political elites’ use of fake news discourse across communications platforms. Int J Commun. 2019;13:4353–75. 92. Asmolov G. The disconnective power of disinformation campaigns. J Int Aff. 2018;71(1.5):69–76. 93. Druckman JN, Klar S, Krupnikov Y, Levendusky M, Ryan JB. Affective polarization, local contexts and public opinion in America. Nat Hum Behav. 2021;5(1):28–38. 94. Lewandowsky S, Stritzke WGK, Freund AM, Oberauer K, Krueger JI. Misinformation, disinformation, and violent conflict: from Iraq and the “War on Terror” to future threats to peace. Am Psychol. 2013;68(7):487–501. 95. Banaji S, Bhat R, Agarwal A, Passsanha N, Pravin MS. WhatsApp vigilantes: an exploration of citizen reception and circulation of WhatsApp misinformation linked to mob violence in India. London School of Economics and Political Science. 2019. http:// eprin ts. lse. ac. uk/ id/ eprint/ 104316. Accessed 23 Apr 2024. 96. Piazza JA. Fake news: The effects of social media disinformation on domestic terrorism. Dyn Asymmetric Conf. 2022;15(1):55–77. 97. Nisbet EC, Kamenchuk O. Russian news media, digital media, informational learned helplessness, and belief in COVID-19 misinformation. Int J Public Opin Res. 2021;33(3):571–90. 98. Grabner-Kräuter S, Bitter S. Trust in online social networks: a multifaceted perspective. Forum Soc Econ. 2015;44:48–68.