This book argues that misinformation poses a multi-faceted threat to knowledge, while arguing that some forms of content moderation risk exacerbating these threats. It proposes alternative forms of content moderation that aim to address this complexity while enhancing human epistemic agency. The proliferation of fake news, false conspiracy theories, and other forms of misinformation on the internet and especially social media is widely recognized as a threat to individual knowledge and, consequently, to collective deliberation and democracy itself. This (...) book argues that misinformation presents a three-pronged threat to knowledge. While researchers often focus on the role of misinformation in causing false beliefs, this deceptive potential of misinformation exists alongside the potential to suppress trust and to distort the perception of evidence. Recognizing the multi-faceted nature of this threat is essential to the development of effective measures to mitigate the harms associated with misinformation. The book weaves together work in analytic epistemology with emerging empirical work in other disciplines to offer novel insights into the threats posed by misinformation. Additionally, it breaks new ground by systematically assessing different forms of content moderation from the perspective of epistemology. Misinformation, Content Moderation, and Epistemology will appeal to philosophers working in applied and social epistemology, as well as scholars and advanced students in disciplines such as communication studies, political science, and social psychology who are researching misinformation. (shrink)

Misinformation is paradigmatically false. However, a great deal of contemporary political content misleads by invoking defective concepts, even when the content is true. This paper offers an account of conceptual misinformation, according to which the use of a concept constitutes misinformation when that concept both fails to adequately capture the world’s structure and receives uptake. Central to my account is the metaphysical notion of “joint-carving” concepts—those that capture the world’s structure and figure into good explanations. This notion (...) can be extended to the social and normative domains, where socially and normatively joint-carving concepts (e.g., “farmer,” “legal free speech”) yield better social and normative explanations than gerrymandered concepts (e.g., “Pacific Islander,” “consequence-free speech”). The need to avoid conceptual misinformation carries practical consequences and imposes a constraint on ameliorative conceptual engineering projects. (shrink)

"Why should we care about having true beliefs? And why do demonstrably false beliefs persist and spread despite consequences for the people who hold them? Philosophers of science Cailin O’Connor and James Weatherall argue that social factors, rather than individual psychology, are what’s essential to understanding the spread and persistence of false belief. It might seem that there’s an obvious reason that true beliefs matter: false beliefs will hurt you. But if that’s right, then why is it irrelevant to many (...) people whether they believe true things or not? In an age riven by "fake news," "alternative facts," and disputes over the validity of everything from climate change to the size of inauguration crowds, the authors argue that social factors, not individual psychology, are what’s essential to understanding the persistence of false belief and that we must know how those social forces work in order to fight misinformation effectively."–Publisher’s description. (shrink)

If citizens are to make enlightened collective decisions, they need to rely on true factual beliefs, but misinformation impairs their ability to do so. Although some cases of misinformation are deliberate and amount to propaganda, cases of inadvertent misinformation are just as problematic in affecting the beliefs and behavior of democratic citizens. A review of empirical evidence suggests that this is a serious problem that cannot entirely be corrected by means of deliberation.

This chapter introduces a novel account of fake news and explains how it differs from other definitions on the market. The account locates the fakeness of an alleged news report in two main aspects related to its production, namely that its creators do not think to have sufficient evidence in favor of what they divulge and they fail to display the appropriate attitude towards the truth of the information they share. A key feature of our analysis is that it does (...) not require that fake news must be circulated with the intention to deceive one’s audience. In this way, our account overcomes a potential limitation of the current philosophical discussion about fake news, which appears to individuate the main problem with this phenomenon in the fact that fake news consumers are misled and misinformed. In contrast, the proposed analysis shows that an additional (and perhaps equally fundamental) problem uncovered by the spread of fake news is a widespread pathological relationship with information, one on which we consume information not to satisfy our interest in the truth but to strengthen our social identities and quench our hunger for social recognition. (shrink)

In this paper, we examine how the availability of massive quantities of data i.e., the “Big Data” phenomenon, contributes to the creation, spread, and harms of online misinformation. Specifically, we argue that a factor in the problem of online misinformation is the evolved human instinct to recognize patterns. While the pattern-recognition instinct is a crucial evolutionary adaptation, we argue that in the age of Big Data, these capacities have, unfortunately, rendered us vulnerable. Given the ways in which online (...) media outlets profit from the spread of misinformation by preying on this pattern-finding instinct, we conceptualize the problem that we identify as a morally objectionable form of “epistemic exploitation.” As we argue, the consumer of digital misinformation is often exploited by having her pattern-recognition instinct used against her. This exploitation is morally objectionable because it deprives her of an epistemic good to which she has a right. This epistemic good is the integrity of the pattern-recognition instinct itself which, we argue, is a capacity that allows us to participate in uniquely human goods. While our primary goal is to bring attention to this form of epistemic exploitation, we conclude by briefly evaluating some general solutions to the growing problem of online misinformation. (shrink)

The issue of how best to combat the negative impacts of misinformation distributed via social media hangs on the following question: are there methods that most individuals can reasonably be expected to employ that would largely protect them from the negative impact that encountering misinformation on social media would otherwise have on their beliefs? If the answer is “yes,” then presumably individuals bear significant responsibility for those negative impacts; and, further, presumably there are feasible educational remedies for the (...) problem of misinformation. However, I argue that the answer is “no.” Accordingly, I maintain that individuals do not bear significant responsibility for the negative impacts at issue; and, further, I maintain that the only effective remedies for the problem of misinformation involve changing the information environment itself. (shrink)

This book serves as a guide to understanding the dynamics of AI in human contexts with a specific focus on the generation, sharing, and consumption of misinformation online. How do humans and AI interact? How is AI shaping our understanding of ourselves and our societies? What are the interaction mechanisms that govern how humans and algorithms contribute to misinformation online? And how do we bridge the gap between ethical considerations and practical realities to make responsible, reliable systems? Exploring (...) these questions, the book empowers humans to make AI design choices that allow them meaningful control over AI and the online sphere. Calling for an interdisciplinary approach toward human-misinformation algorithmic interaction that focuses on building methods and tools that robustly deal with complex psychological/social phenomena, the book offers a compelling insight into the future of AI-based society. (shrink)

In their recent book, Inducing Immunity (2024), Roland Pierik and Marcel Verweij develop an account of collective immunization according to which liberal-democratic governments are sometimes justified in restricting people's freedom to refuse vaccination. In this commentary, I focus on a specific argument in the book about misinformation, freedom of expression and mandatory vaccination. Pierik and Verweij argue that, when vaccine misinformation threatens vaccination rates, governments—in order to increase or maintain vaccination rates—should consider mandatory vaccination before restricting freedom of (...) expression. I summarize the argument and raise two issues to help advance the debate. (shrink)

This paper addresses the following question: under what conditions is online content moderation permissible? Often stringent content moderation policies are invoked when online misinformation is linked to serious harm. Here I show the complexities involved in allowing for this kind of content moderation. The conclusion will be that, when a clear link between misinformation and serious harm cannot be established, social media companies may instead adopt lighter forms of content moderation, such as accuracy nudging, to curb misinformation.

In the current digital era, reliance on technology for communication and the gathering and dissemination of information is growing. However, the information disseminated can be misleading or false. Nurses tend to be trusted by the public, but not all information brought to the public forum is well-informed. Ill-informed discussions have resulted in harm to individuals who take such information as fact and act on it. As technology continues to evolve and fact versus fiction becomes more challenging to discern, it is (...) critical that nurses recognize their ethical responsibility to the public in providing information for which sound evidence exists. This analysis will explore medical misinformation through concepts such as confirmation bias and the politicization of science. Also, the impact of nurses not recognizing the power and responsibility associated with using their credentials in public fora, even when the central motivator is that they believe they are helping other individuals. Using nursing goals and perspectives, we will discuss the ethical responsibility of nurses to be aware of the soundness of what they think they know. Utilizing ideas of professional responsibilities, as outlined by professional codes of ethics as well as the ethical principles of non-maleficence and veracity, we explore the problem of nurses propagating misinformation and suggest strategies to enhance nurse awareness of their ethical responsibilities for veracity and transparency regarding what is known and what is not. (shrink)

This paper addresses the following question: under what conditions is online content moderation permissible? Often stringent content moderation policies are invoked when online misinformation is linked to serious harm. Here I show the complexities involved in allowing for this kind of content moderation. The conclusion will be that, when a clear link between misinformation and serious harm cannot be established, social media companies may instead adopt lighter forms of content moderation, such as accuracy nudging, to curb misinformation.

Alarmist narratives about online misinformation continue to gain traction despite evidence that its prevalence and impact are overstated. Drawing on research examining the use of big data in social science and reception studies, we identify six misconceptions about misinformation and highlight the conceptual and methodological challenges they raise. The first set of misconceptions concerns the prevalence and circulation of misinformation. First, scientists focus on social media because it is methodologically convenient, but misinformation is not just a (...) social media problem. Second, the internet is not rife with misinformation or news, but with memes and entertaining content. Third, falsehoods do not spread faster than the truth; how we define (mis)information influences our results and their practical implications. The second set of misconceptions concerns the impact and the reception of misinformation. Fourth, people do not believe everything they see on the internet: the sheer volume of engagement should not be conflated with belief. Fifth, people are more likely to be uninformed than misinformed; surveys overestimate misperceptions and say little about the causal influence of misinformation. Sixth, the influence of misinformation on people’s behavior is overblown as misinformation often “preaches to the choir.” To appropriately understand and fight misinformation, future research needs to address these challenges. (shrink)

If political fake news is a serious concern for democratic politics, no less worrisome is scientific news with patently distorted content. Prima facie, scientific misinformation partially escapes the definition of fake news provided by empirical and philosophical analysis, mainly patterned after political disinformation. Most notably, we aim to show that people are often unaware not only of disseminating, but also of producing false or misleading information. However, by leveraging the philosophical and psychological literature, we advance some reasons for keeping (...) scientific misinformation under the same umbrella, broadening the definition of fake news in order to account for it as well. In concluding, we shall advance some ideas on how to reform scientific communication, which may help to address the issue of scientific misinformation. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

Standard accounts of misinformation require that it is either false or misleading, in the sense that it leads people to false beliefs. But many examples of misinformation involve true information that leads people to true beliefs. So, I propose a new theory of misinformation: misinformation is information that is epistemically harmful in the sense that it is disposed to reduce the overall quality of a subject’s epistemic position. This includes not only causing the subject to form (...) a false belief, but also causing the subject to form beliefs that are otherwise epistemically deficient or to abandon or reduce confidence in epistemically good beliefs. This account improves on standard accounts in two ways. First, it more closely matches intuitive judgments about what counts as misinformation, including cases of failed deception and information that is spread without regard for its truth. Second, it provides a natural account of what is wrong with spreading misinformation: it contributes to an epistemically hostile environment in which we are more likely to become epistemically worse off when we gather more information. (shrink)

Are most people sincere when they share misinformation and conspiracies online? This question, while natural and important, is difficult to answer for obvious reasons. But it also applies poorly to one of the main vehicles for misinformation—memes. And it can be ambiguous; as a result, we should be mindful of two distinctions. First, a distinction between belief and a related propositional attitude, commitment. And second, the distinction between the propositional content of an attitude and what I will call (...) its political meaning. I will suggest that these distinctions not only can help us understand how we communicate online, but they also suggest a lesson about what we should be focusing on when fighting misinformation. (shrink)

Machine learning models of misinformation detection are increasingly being used and yet their risks have been under analyzed, requiring insights from philosophy of science and political philosophy. A taxonomy of types of risks and simple models estimating them is provided that is sensitive to the value-laden features of judgments of misinformation and weighted by a potential item of misinformation’s impact on respective stakeholders. Failing to do so is incompatible with a variety of civil virtues that are desired (...) by not only liberal democratic societies but authoritarian ones as well, suggesting the general applicability of this taxonomy of risks to contemporary and future societies. (shrink)

The article explores the concept of infodemics during the COVID-19 pandemic, focusing on the propagation of false or inaccurate information proliferating worldwide throughout the SARS-CoV-2 health crisis. We provide an overview of disinformation, misinformation and malinformation and discuss the notion of “fake news”, and highlight the threats these phenomena bear for health policies and national and international security. We discuss the mis-/disinformation as a significant challenge to the public health, intelligence, and policymaking communities and highlight the necessity to design (...) measures enabling the prevention, interdiction, and mitigation of such threats. We then present an overview of selected opportunities for applying technology to study and combat disinformation, outlining several approaches currently being used to understand, describe, and model the phenomena of misinformation and disinformation. We focus specifically on complex networks, machine learning, data- and text-mining methods in misinformation detection, sentiment analysis, and agent-based models of misinformation spreading and the detection of misinformation sources in the network. We conclude with the set of recommendations supporting the World Health Organization’s initiative on infodemiology. We support the implementation of integrated preventive procedures and internationalization of infodemic management. We also endorse the application of the cross-disciplinary methodology of Crime Science discipline, supplemented by Big Data analysis and related information technologies to prevent, disrupt, and detect mis- and disinformation efficiently. (shrink)

This chapter explores how online communication, particularly on social media, reshapes the reputational incentives that motivate speakers to communicate truthfully. Drawing on costly signalling theory (CST), it examines how online contexts alter the social mechanisms that sustain honest communication. Key characteristics of online spaces are identified and discussed, namely (i) the presence of novel speech acts like reposting, (ii) the gamification of communication, (iii) information overload, (iv) the presence of anonymous and unaccountable sources and (v) the increased reach and persistence (...) of online communication. Both epistemic pitfalls and potential benefits of these features are discussed, identifying promising avenues for further empirical investigation, and underscoring CST's value for understanding and tackling online misinformation. (shrink)

What happens if the data fed to AI are biased? What happens if the response of a chatbot spreads misinformation? Unlike many people hope, AI is as biased as humans are. Bias can originate from various venues, including but not limited to the design and unintended or unanticipated use of the algorithm or algorithmic decisions about the way data are coded, framed, filtered, or analyzed to train machine learning. Algorithmic bias has been widely seen in advertising, content recommendations, and (...) search engine results. Algorithmic prejudice has been found in cases ranging from political campaign outcomes to the proliferation of fake news and misinformation. It has also surfaced in health care, education, and public service, aggravating existing societal, socioeconomic, and political biases. These algorithm-induced biases can exert negative impacts on a range of social interactions, ranging from unintended privacy infringements to solidifying societal biases of gender, race, ethnicity, and culture. The significance of the data used in training algorithms should not be underestimated. Humans should play a part in the datafication of algorithms, as preventing the spread of misinformation is difficult by technology alone, especially considering the rate at which information can spread online. (shrink)

This paper argues that the well-established fact of political irrationality imposes substantial constraints on how governments may combat the threat of political misinformation. Though attempts at regulating misinformation are becoming increasingly popular, both among policymakers and theorists, I intend to show that, for a wide range of anti-misinformation interventions (collectively termed “debunking” and “source labeling”), these attempts ought to be abandoned. My argument relies primarily on the fact that most people process politically-relevant information in biased and motivated (...) ways. Since debunking or factual correction of politically relevant misinformation (as well as source labeling) themselves consist of providing politically-relevant information, they are also very likely to be processed in irrational ways. This makes it extremely difficult to effectively correct people’s political beliefs and political information processing. Since governments should not pursue policies likely to be futile, they should refrain from mandating such interventions. My conclusion is of relevance to considerable literature in digital ethics of misinformation. It shows that many celebrated works in the field ignore political irrationality and fail to consider its implications. (shrink)

Three interdependent factors are behind the current Covid-19 pandemic distorted narrative: (1) science´s culture of “publish or perish”, (2) misinformation spread by traditional media and social digital media and (3) distrust of technology for tracing contacts and its privacy-related issues. In this short paper, I wish to tackle how these three factors have added up to give rise to a negative public understanding of science in times of a health crisis, such as the current Covid-19 pandemic and finally, how (...) to confront all these problems. (shrink)

This chapter addresses the relationship between misinformation and disagreement. We begin by arguing that one traditional bogeyman in this domain, ideological polarization, does not account for the many problems that have been documented. Instead, affective polarization seems to be the root cause of most of these problems. We then discuss the relationships between moral outrage, misinformation, and affective polarization. We next turn to the political implications of affective polarization and conclude by discussing some potential solutions to the problems (...) that arise in this area. (shrink)

Misinformation can be a direct cause of radicalization due to its tendency to trigger strong emotions. Aggressive messages that arouse anxiety can be highly persuasive—messages that point to a threat, particularly one that is sensitive and socially hot, create a cognitive drive for more content about that threat and generate support for responsive action. This chapter critically examined the role that social media algorithms play in recommending extreme content. TikTok’s role in fostering radicalized content was examined by tracing how (...) users become radicalized on TikTok and how its recommendation algorithms drive this radicalization. It identified the social, technological, and psychological factors that contribute to the radicalization of ideological biases on social media and proposed a conceptual lens through which to analyze and predict such radicalization. The results revealed that the pathways by which users access far-right content are manifold and that a large part of this can be ascribed to platform recommendations through a positive feedback loop. The results are consistent with the proposition that the generation and adoption of extreme content on TikTok largely reflect the user’s input and interaction with a platform. It is argued that some features of misinformation are likely to promote radicalization among users. It concludes how trends in artificial intelligence (AI)-based content systems are forged by an intricate combination of user interactions, platform intentions, and the interplay dynamics of a broader AI ecosystem. (shrink)

Orthodox Humeans about normative reasons for action believe that there are no rational principles governing the substantive content of desire. But they also believe that desires with misinformed content should be rejected and cannot be the proper subjective sources of normative reasons for action. These two ideas, I argue, in fact stand in tension with each other: The Humean rejection of misinformed desire actually has to invoke a feasibility principle for desire, a semi-substantive rational principle that is already built into (...) the very conceptions of rationality and desire that underlie orthodox Humeanism. This rational principle then provides a new account of the substantive rationality of desire, which in turn has some interesting implications in metaethics and first-order normative theories. (shrink)

In vice epistemology, bad epistemic outcomes, such as maintaining false beliefs, are interpreted as indicators of blameworthy irrationality. Conversely, a growing trend in philosophical psychology advocates for environmentalist explanations, suggesting these outcomes emerge because rational cognitive processes of faultless individuals falter due to polluted environmental inputs. Building on concrete examples, I first offer a systematic analysis of the relative explanatory merits of that environmentalist project. I then use this analysis to advance the rationality debate, which has recently been identified as (...) stagnating due to an observational equivalence between environmentalist and vice accounts. Although the conceptual imprecision of vice epistemology has frequently been critiqued, the framework developed here reveals that environmentalism is (also) unable to meet various theoretical desiderata. I show why this is so and argue that, to make progress, environmentalism needs a more substantive conception of epistemic rationality. To this end, I propose that a closer engagement with questions of cognitive agency – how rational creatures can “make up” their minds about what to believe – could enable the necessary progress. (shrink)

Purpose Although previous research has explored links between misinformation and polarisation, it often overlooks the central role of social identity mechanisms. This study aims to examine the relationship between misinformation and polarisation, focusing on the underlying mechanisms of social identity and explaining how identity-driven processes shape the acceptance and dissemination of misinformation, as well as the intensification of opinion and affective polarisation. Design/methodology/approach This study uses a narrative integrative literature review approach, drawing on the social identity perspective (...) and synthesising empirical evidence to develop a conceptual framework that explains the identity-based cognitive, emotional and behavioural processes connecting misinformation and polarisation. Findings The review identifies three key findings. Firstly, social identity drives group-based opinion and affective polarisation, and significantly increases the likelihood that individuals accept and share misinformation consistent with their group stance. Secondly, although misinformation and polarisation have a reciprocal causal relationship, their connection is fundamentally rooted in social identity mechanisms. Thirdly, the expressive function of identity is a key factor, indicating that both misinformation and polarisation are essentially derivative manifestations of social identity. Originality/value By identifying identity mechanisms as the root cause of these dynamics, this study offers an integrated analytical framework that clarifies the foundational role of social identity in misinformation and polarisation. This framework can guide future research and inform the design of targeted interventions to reduce the social and ethical risks of misinformation in digital media environments. (shrink)

The diffusion of misinformation has garnered considerable attention in our society. As algorithms have been considered one of the major drivers behind the spread and amplification of misinformation, it is useful to understand the effects of these algorithms on misinformation sharing and the manner in which they spread it. This chapter examines the psychological, cognitive, and social factors involved in the processing of misinformation people receive through algorithms and artificial intelligence. Modeling cognitive processes has long been (...) of interest for understanding user reasoning, and many theories from different fields have been formalized into cognitive models. Drawing on theoretical insights from information processing theory with the concept of diagnosticity, it examines how perceived normative values influence a user’s perceived diagnosticity and likelihood of sharing information and whether explainability further moderates this relationship. The findings showed that users with a high heuristic processing of normative values and positive diagnostic perception were more likely to proactively discern misinformation. Users with a high cognitive ability to understand information were more likely to discern it correctly and less likely to share misinformation online. When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation. With this focus on misinformation processing, this chapter provides theoretical insights and relevant recommendations for firms to be more resilient in protecting themselves from the detrimental impact of misinformation. (shrink)

The rapid spread of misinformation online can be attributed to bias in human decision-making, facilitated by algorithmic processes. The area of human-computer interaction has contributed a mechanism of such biases that can be addressed by the design of system interventions. For example, the principle of nudging refers to sophisticated modifications in the choice architecture that can change user behaviors in desired or directed ways. This chapter discusses the design of nudging interventions in the context of misinformation, including a (...) systematic review of the use of nudging in human-AI interaction that has led to a design framework. By using algorithms that work invisibly, nudges can be maneuvered in misinformation to individuals, and their effectiveness can be traced and attuned as the algorithm improves from user feedback based on a user’s behavior. It seeks to explore the potential of nudging in decreasing the chances of consuming and spreading misinformation. The key is how to ensure that algorithmic nudges are used in an effective way and whether the nudge could also help to achieve a sustainable way of life. This chapter discusses the principles and dimensions of the nudging effects of AI systems on user behavior in response to misinformation. (shrink)

Amidst the misinformation climate about trans people and their health care that dominates policy and social discourse, autonomy‐based rationales for gender‐affirming care for trans and nonbinary youth are being called into question. In this commentary, which responds to “What Is the Aim of Pediatric ‘Gender‐Affirming’ Care?,” by Moti Gorin, we contextualize the virulent ideas circulating in misinformation campaigns that have become weaponized for unprecedented legal interference into standard health care. We conclude that the current legal justifications for upending (...) gender‐affirming care gloss over how this health care field meets conventional evidentiary standards and aligns protocols with most other fields of medicine. Refusal to offer gender‐affirming care is more harmful than centralizing trans and nonbinary people's health autonomy. (shrink)

The specificity of misinformation about Covid-19 which was outspread in the media landscape of Ukrainian society was demonstrated in the article. The authors relying on the basics of postmodern theory within interdisciplinary discourse trace the means of forming misinformation and its influence on changing worldview landmarks of humanity. The authors underline that information in the postmodern conception of Jean Baudrillard is also capable to destroy its own content, communication and social ground. Misinformation about Covid-19 is «a stage (...) setting of communication» since it only creates insight into sense and is outspreaded with different channels with usage of photographic and video materials, emotional headlines and messages. Fake materials which have been outspreaded in online media and social networks are analyzed in the article. The authors found out that fake messages outspreaded in Ukrainian media refer to the following topic. It is stated that the quick expansion of misinformation was facilitated by the fact that the nationals of Ukraine took information without criticism, without fact-checking, since they moved to a new space of life of the individual and made «a stage setting of sense». (shrink)

Media attention to retracted research suggests that a substantial number of papers are corrupted by misinformation. In reality, every paper contains misinformation; at issue is whether the balance of correct versus incorrect information is acceptable. This paper postulates that analysis of retracted research papers can provide insight into medical misinformation, although retracted papers are not a random sample of incorrect papers. Error is the most common reason for retraction and error may be the principal cause of (...) class='Hi'>misinformation as well. Still, one-quarter of retracted papers are fraudulent, and misinformation may also arise through fraud. This paper hypothesises that error and fraud are the main sources of misinformation and that error is more common than fraud. Retraction removes misinformation from the literature; bias is non-retracted misinformation. Bias arises when scientific impropriety results in false research findings. Impropriety can involve experimental design, data collection, data analysis, or data presentation. Yet impropriety also arises through earnest error or statistical naiveté; not all bias is fraud. Several measures are proposed to minimise misinformation in the medical literature, including: greater detail in the clinical trial registry, with rigorous definition of inclusion and exclusion criteria and primary endpoints; clear statistical criteria for every aspect of clinical trials, especially sample size; responsibility for data integrity that accrues to all named authors; increased transparency as to how the costs of research were paid; and greater clarity as to the reasons for retraction. Misinformation can arise without malicious intent; authors of incorrect papers are owed a presumption of incompetence, not malice. (shrink)

Purpose Health misinformation poses severe risks to people’s health decisions and outcomes. A great deal of research in this area has focused on debunking misinformation and found limited effects of correctives after misinformation exposure. The research on prebunking strategies has been inadequate. Most has focused on forewarning and enhancing literacy skills and knowledge to recognize misinformation. Part of the reason for the inadequacy could be due to the challenges in conceptualizing and measuring knowledge. This study intends (...) to fill this gap and examines various types of knowledge, including subjective knowledge, cancer literacy, persuasion knowledge and media literacy. This study aims to understand how knowledge may moderate the effect of misinformation exposure on misbeliefs. Design/methodology/approach An online experiment with a basic experimental design (misinformation exposure: health misinformation vs factual health message) was conducted. The authors measured and tested the moderating role of different types of knowledge (subjective knowledge, cancer literacy, persuasion knowledge and media literacy) separately to improve the understanding of their role in combatting online health misinformation. Findings This study found that a higher level of cancer literacy and persuasion knowledge helped people identify misinformation and prevented them from being persuaded by it. A higher level of subjective knowledge, however, reduced the recognition of misinformation, thereby increasing the likelihood of being persuaded by it. Media literacy did not moderate the mediation path. Originality/value This study differentiates the role different types of knowledge may have played in moderating the influence of health misinformation. It contributes to a strategic development of interventions that better prepare people against the influence of health misinformation. (shrink)

Public support for responses to the coronavirus pandemic has sharply diverged on partisan lines in many countries, with conservatives tending to oppose lockdowns, social distancing, mask mandates and vaccines, and liberals far more supportive. This polarization may arise from the way in which the attitudes of each side is echoed back to them, especially on social media. In this paper, I argue that echo chambers are not to blame for this polarization, even if they are causally responsible for it. They (...) are not to blame, because belief calibration in an echo chamber is a rational process; moreover, the epistemically constitutive properties of echo chambers are not optional for epistemically social animals like us. There is no special problem of echo chambers; rather, there is a problem of misleading evidence (especially higher-order evidence). Accordingly, we ought to respond to misinformation about COVID neither by attempting to dismantle echo chambers nor by attempting to make people more rational, but rather by attempting to supplant unreliable higher-order evidence with better evidence. (shrink)

“The bird is freed.” So tweeted Elon Musk after purchasing Twitter, thus ushering in a promised era of free speech on the platform. But is unrestricted free speech what we should want on a platform like Twitter (now “X”)? The restrictions Musk was promising to eliminate were intended to address what are, plausibly, serious problems in need of serious solutions – especially hate speech and misinformation. Isn’t it better to moderate such speech rather than let it spin out of (...) control, something that can have real consequences? This case study argues that at least when the aim is to combat misinformation, the answer is often ‘no’; censorship offends against reasonable democratic civility and is often unlikely to work anyway. To make my case, I draw on scholarship on the history of information flow in America. The internet and social media are often blamed for misinformation, but at least in the United States, this appears to badly miss the mark. Insofar as the problem is real and not exaggerated, its clearest origins are a breakdown in trust that began in or around the 1950s. Once we see this, it is a small leap to recognize that censorship cannot be the solution to these problems; a healthy climate of trust cannot be rebuilt while citizens declare one another to be beyond the pale and demand or embrace removing one another from the public forum. (shrink)

In experiments concerning the misinformation effect, participants first watch some original material, e.g. a video clip, and read a description that in the experimental group contains information inconsistent with the video clip. Afterwards, all participants answer questions about the video. Typically, the misled group more often reports erroneous misleading information than the non-misled one.Theoretical explanations of this effect are usually formulated in terms of the cognitive theories of memory. This article presents three experiments that demonstrate that the misinformation (...) effect can occur even if the memory of the original and postevent materials is correct. In the experiments, after watching a video clip, reading a narrative about it, and answering questions about the video, the participants were debriefed and required to indicate questions in which they noticed differences between the video and the narrative, as well as provide answers about the original and postevent materials. A substantial number of the participants yielded to the misinformation effect in the memory test even though they had correct memory about the original (and postevent) materials. The discussion emphasizes the need of the social influence framework to explain these results. Key message: the misinformation effect is important for applied forensic eyewitness psychology. To get a better understanding of this effect, there is a need to study it not only in terms of the cognitive psychology of memory, but also from the perspective of social psychology, because in many cases witnesses give wrong answers even when remembering the correct information. (shrink)

No one is completely immune to misinformation because of how human cognition is built and how misinformation takes advantage of it. Often, using nudges to help steer users into fact-checking the information is much more effective than detecting misinformation. This chapter presents empirical work on the design of nudge interventions in the context of misinformation. Applying the nudge principle to misinformation, it suggests that different cognitive biases that humans are vulnerable to can be leveraged for (...) the design of algorithmic interventions that reduce the consumption and spread of misinformation. The findings from an experiment revealed significant main and interaction effects, indicating that algorithmic source effects are present in the process of nudge sensemaking. Misinformation sharing intention was largely lower for nonalgorithmic news than for algorithm-based news, but there was a greater drop in algorithmic news when nudging was employed. Moderation from algorithmic trust was found, and users’ trust in algorithmic media amplified the nudge effect only for news from algorithmic media and not nonalgorithmic online media sources. The results of our study confirm previous literature that underlined the role of nudging in influencing news sharing. Source credibility has an impact on misinformation sharing on social media, and nudge credibility encourages discerning and acknowledging misinformation. The findings contribute to the design implications of nudging interventions in the context of misinformation, as well as prototyping a range of nudging mechanisms with the goal of evaluating their proximal effects on human behavior in AI. (shrink)

ChatGPT has opened a new front in the fake news wars. This chapter is motivated by the rapidly improving capabilities and accessibility of generative AI and rapidly increasing misinformation problems. Misinformation is by no means a new phenomenon, yet its trend is highlighted by the emergence of AI. It might be useful to see misinformation in the context of a new and rapidly evolving AI landscape, which has facilitated the spread of unparalleled volumes of information at lightning (...) speeds. This chapter discusses the misinformation effect by examining how users process and respond to misinformation in generative artificial intelligence (GenAI) contexts. Drawing on the heuristic-systematic model, it examines the factors influencing a user’s diagnosticity and likelihood of sharing information and whether explanatory heuristics moderate this relationship. The findings showed that users with high heuristic processing of ethical values and positive diagnostic perception were more likely to proactively discern misinformation than users with low heuristic processing and low diagnostic perception. When exposed to misinformation from GenAI, users’ construed diagnosticity of misinformation can be accurately predicted from their understanding of ethical values. With this focus on misinformation processing, this chapter provides theoretical insights and relevant recommendations for firms to be more resilient in protecting users from the detrimental impact of misinformation. (shrink)

This chapter introduces the principle of diversity-aware AI and discusses the need to develop recommendation models to embed AI with diversity awareness to mitigate misinformation. Free and plural ideas are key to addressing misinformation and informing users. A key indicator of the healthy online ecosystem is the existence of diversity of ideas and others’ perspectives. Exposure to diverse sources of news promotes tolerance, social cohesion, and harmonious accord of different ideologies, perspectives, and cultures. Diversity in news recommender systems (...) (NRS) is perceived as a major issue for the preservation of a healthy democratic discourse. In this light of importance, this chapter proposes a conceptual framework for personalized recommendation nudges that can promote diverse news consumption on online platforms. It empirically tests the effects of algorithmic nudges by examining how users make sense of algorithmic nudges and how nudges influence users’ views on personalization and attitudes toward news diversity. The findings show that algorithmic nudges play a key role in understanding normative values in NRS, which then influence the user’s intention to consume diverse news. The findings imply the personalization paradox that personalized news recommendations can enhance and decrease user engagement with the systems. This paradox provides conceptual and operational bases for diversity-aware NRS design, enhancing the diversity and personalization of news recommendations. It proposes a conceptual framework of algorithmic nudges and news diversity, and from there, we develop theoretically grounded paths for facilitating diversity and inclusion in NRS. (shrink)

Misinformation in various guises has become a significant concern in contemporary society and it has been implicated in several high-impact political events over the past years, including Brexit, the 2016 American elections, and bungled policy responses to the Covid-19 pandemic in some countries. In this paper, I draw on resources from contemporary social epistemology to clarify why and how misinformation is epistemically bad. I argue that its negative effects extend far beyond the obvious ones of duping individuals with (...) false or misleading beliefs. Misinformation has systemic effects on our information environments, making all of us worse off, including the epistemically vigilant. This paper does not offer measures or policies to fight misinformation, but aims to contribute to the prior goal of better understanding what's bad about misinformation. In doing so, it lays the groundwork for ameliorative projects. (shrink)

The rapid advancement of generative artificial intelligence (AI) has introduced both opportunities and challenges in the fight against misinformation. This scoping review synthesizes recent empirical studies to explore the dual role of generative AI—particularly large language models (LLMs)—in the generation, detection, mitigation, and impact of misinformation. Analyzing 24 empirical studies, our review suggests that LLMs can generate highly convincing misinformation, often exploiting cognitive biases and ideological leanings of the audiences, while also demonstrating the ability to detect false (...) claims and enhance users’ resistance to misinformation. Mitigation efforts show mixed results, with personalized corrections proving effective but safeguards inconsistently applied. Additionally, exposure to AI-generated misinformation was found to reduce trust and influence decision-making. This review underscores the need for standardized evaluation metrics, interdisciplinary collaboration, and stronger regulatory measures to ensure the responsible use of generative AI in the information ecosystem. (shrink)

AI-enabled services, such as chatbots and generative systems, are often unable to generate correct information per user request, thus creating user resistance and preventing the smooth diffusion of AI services. Previous research has mostly addressed how to improve AI responses but fails to consider user resistance against misinformation from AI. Based on inoculation theory and a heuristic systematic model, this chapter discusses the cognitive mechanisms of inoculation effects on using AI chatbots by addressing questions on how users construe inoculation (...) messages and how the messages influence users’ resistance against misinformation. How inoculation confers resistance to users provides important implications for theory and practice. The chapter found that inoculation messages alleviate the negative effects of misinformation from AI chatbots on user interaction. It renders a critical perspective of how the theory can be conceptually extended to misinformation and how the theoretical frame can be used practically. (shrink)

It is wrong to force refugees to return to the countries they fled from. It is similarly wrong, many argue, to force migrants back to countries with life-threatening conditions. I argue that it is additionally wrong to help such refugees and migrants voluntarily return whilst failing to inform them of the risks. Drawing on existing data, and original data from East Africa, I describe distinct types of cases where such a wrong arises. In ‘Misinformation Cases’ officials tell refugees that (...) it is safe to return, when it is not, and refugees return who would have otherwise stayed. In ‘Omission Cases’ officials do not provide any information on countries of origin, and this omission causes refugees to repatriate. In ‘Relevancy Cases’ refugees are misinformed or uninformed, but would have returned even if better informed. In all of these cases, at least some state officials are blameworthy for their failure to inform refugees, and are engaging in a form of wrongful immigration control. (shrink)

It is well known that informational theories of representation have trouble accounting for error. Informational semantics is a family of theories attempting a naturalistic, unashamedly reductive explanation of the semantic and intentional properties of thought and language. Most simply, the informational approach explains truth-conditional content in terms of causal, nomic, or simply regular correlation between a representation and a state of affairs. The central work is Dretske, and the theory was largely developed at the University of Wisconsin by Fred Dretske, (...) Dennis Stampe, and Berent Enc. Recently, informational semantics has roamed far beyond its Wisconsin home, and built a sizeable collection of followers. Converts include Jerry Fodor, Robert Stalnaker and, less faithfully, Paul and Patricia Churchland and Hartry Field. But for some years informational semantics has been hounded by a problem with error – the classic presentation is Fodor – and no other problem has hounded the theory so persistently. (shrink)

Misinformation can have significant societal consequences. For example, misinformation about climate change has confused the public and stalled support for mitigation policies. When people lack the expertise and skill to evaluate the science behind a claim, they typically rely on heuristics such as substituting judgment about something complex (i.e. climate science) with judgment about something simple (i.e. the character of people who speak about climate science) and are therefore vulnerable to misleading information. Inoculation theory offers one approach to (...) effectively neutralize the influence of misinformation. Typically, inoculations convey resistance by providing people with information that counters misinformation. In contrast, we propose inoculating against misinformation by explaining the fallacious reasoning within misleading denialist claims. We offer a strategy based on critical thinking methods to analyse and detect poor reasoning within denialist claims. This strategy includes detailing argument structure, determining the truth of the premises, and checking for validity, hidden premises, or ambiguous language. Focusing on argument structure also facilitates the identification of reasoning fallacies by locating them in the reasoning process. Because this reason-based form of inoculation is based on general critical thinking methods, it offers the distinct advantage of being accessible to those who lack expertise in climate science. We applied this approach to 42 common denialist claims and find that they all demonstrate fallacious reasoning and fail to refute the scientific consensus regarding anthropogenic global warming. This comprehensive deconstruction and refutation of the most common denialist claims about climate change is designed to act as a resource for communicators and educators who teach climate science and/or critical thinking. (shrink)

Experts are sharply divided concerning the prevalence and influence of misinformation. Some have emphasized the severe epistemic and political threats posed by misinformation and have argued that some such threats have been realized in the real world. Others have argued that such concerns overstate the prevalence of misinformation and the gullibility of ordinary persons. Rather than taking a stand on this issue, I consider what would follow from the supposition that this latter perspective is correct. I argue (...) that, if the prevalence and influence of misinformation are indeed overstated, then many reports as to the prevalence and influence of misinformation constitute a kind of higher-order misinformation. I argue that higher-order misinformation presents its own challenges. In particular, higher-order misinformation, ironically, would lend credibility to the very misinformation whose influence it exaggerates. Additionally, higher-order misinformation would lead to underestimations of the reasons favoring opposing views. In short, higher-order misinformation constitutes misleading higher-order evidence concerning the quality of the evidence on which individuals form their beliefs. (shrink)

The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, (...) we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size. (shrink)

Moral outrage is a powerful motivational force that drives social change and collective action. Social media has become a primary arena for the expression and amplification of moral outrage, where users’ emotional reactions often translate into rapid information sharing. Yet this process also amplifies the spread of moralised misinformation, posing substantial social challenges. While moral outrage plays a central role in such dynamics, the distinct roles of its core components, moral anger and disgust, remain unclear. Grounded in the Elaboration (...) Likelihood Model and moral emotion theories, our study aimed to investigate how moral anger and moral disgust interact with source credibility to influence online information sharing. Across three experiments, we systematically manipulated message features (source credibility, moral transgression severity) and participants’ cognitive–affective states (attention to morality versus accuracy, moral emotional states). Results indicated greater transgression severity increased willingness to share (WTS), particularly when morality was emphasised (Study 1). While higher source credibility generally increases WTS (all studies), moral anger (not disgust) overrode credibility cues (Study 2). Drift-diffusion model revealed that anger induction accelerated decision-making and lowered decision thresholds (Study 3). These findings identify moral anger as a peripheral cue that facilitates impulsive sharing and offers insights for emotion-based misinformation interventions. (shrink)