How People Weave Online Information Into Pseudoknowledge

Deep Storytelling: Collective Sensemaking and Layers of Meaning in U.S. Elections

Misinformation and disinformation about elections remain pressing concerns for researchers, policymakers, and the public. Critics, however, argue that fears surrounding these issues are exaggerated due to a lack of evidence of impact. This debate highlights the challenges inherent in assessing the impacts of misinformation, as the drivers of false and misleading content often exist in the context of a specific claim. To address this issue, we examined false and misleading information surrounding the 2020 and 2022 U.S. national elections, focusing on the contextual features of online conversations that fueled various rumors. We developed two qualitative codebooks, creating the second after realizing that the first, which labeled individual tweets, failed to capture broader rumoring dynamics. By integrating multi-layered qualitative coding with thematic analysis and quantitative visualizations, we show how influencers, political elites, and audiences collaboratively told deep stories from 2020 through 2022. As these stories were told, audiences interpreted events in 2022 through the lens of the 2020 story, guided by influencers' cues, leading to an evolution in storytelling style between the two election cycles. This ongoing performance was tailored to align with the incentive structures, affordances, and attention economy of social media. We combine deep stories with theories of collective sensemaking and rumoring, creating a framework to better assess the contextual features surrounding false and misleading information.

Facts and Myths about Misperceptions

Misperceptions threaten to warp mass opinion and public policy on controversial issues in politics, science, and health. What explains the prevalence and persistence of these false and unsupported beliefs, which seem to be genuinely held by many people? Though limits on cognitive resources and attention play an important role, many of the most destructive misperceptions arise in domains where individuals have weak incentives to hold accurate beliefs and strong directional motivations to endorse beliefs that are consistent with a group identity such as partisanship. These tendencies are often exploited by elites who frequently create and amplify misperceptions to influence elections and public policy. Though evidence is lacking for claims of a “post-truth” era, changes in the speed with which false information travels and the extent to which it can find receptive audiences require new approaches to counter misinformation. Reducing the propagation and influence of false claims will require further efforts to inoculate people in advance of exposure (for example, media literacy), debunk false claims that are already salient or widespread (for example, fact-checking), reduce the prevalence of low-quality information (for example, changing social media algorithms), and discourage elites from promoting false information (for example, strengthening reputational sanctions).

Beyond Misinformation: Social Media and the Sculpting of Epistemic Agency

ABSTRACT The dominant account of social media's epistemic harms focuses on the spread of misinformation and the production of false beliefs at scale. This paper argues that the misinformation narrative, whatever its merits, has obscured a deeper and more intimate form of epistemic damage. Dominant social media platforms do not merely supply bad inputs to stable epistemic machinery. They reshape the machinery itself. I argue that social media platforms are powerful engines for sculpting epistemic agency via two mechanisms: normative learning, through which users internalize descriptive and injunctive norms about how to interact with evidence, and reinforcement learning, through which habitual patterns of epistemic response are developed. In these ways, platforms can reshape users' epistemic styles, including their testimonial policies, evidential thresholds for belief revision, and theoretical values governing how they select explanations. This should trouble us, as the constraints driving which styles are produced are systematically orthogonal to truth and ignore information about the environments agents inhabit. This discussion identifies a neglected form of epistemic hostility: not the exploitation of existing cognitive vulnerabilities to implant false beliefs, but the production of new vulnerabilities that can then be exploited. From this, more and better evidence cannot save us.

Capturing our Attention

What are the harms of misinformation? It’s widely believed that it causes false belief, including false beliefs in quite bizarre claims. I argue that this threat is greatly exaggerated: while plaus...

How Social Media Rewards Misinformation

A majority of false stories are spread by a small number of frequent users, suggests a new study co-authored by Yale SOM’s Gizem Ceylan. But they can be taught to change their ways.

Fake news spreads like an epidemic. Here’s how to stop it.

Researchers study fake news the same way they study the spread of disease. When misinformation “goes viral”, it is not just a metaphor. It is what’s actually happening. via @straits_times str.sg/viz-misinformation

The Liar’s Dividend: Can Politicians Claim Misinformation to Evade Accountability?

This study addresses the phenomenon of misinformation about misinformation, or politicians "crying wolf"' over fake news. Strategic and false claims that stories are fake news or deepfakes may benefit politicians by helping them maintain support after a scandal. We posit that this benefit, known as the "liar's dividend," may be achieved through two politician strategies: by invoking informational uncertainty or by encouraging oppositional rallying of core supporters. We administer five survey experiments to over 15,000 American adults detailing hypothetical politician responses to stories describing real politician scandals. We find that claims of misinformation representing both strategies raise politician support across partisan subgroups. These strategies are effective against text-based reports of scandals, but are largely ineffective against video evidence and do not reduce general trust in media. Finally, these false claims produce greater dividends for politicians than alternative responses to scandal, such as remaining silent or apologizing.

Fakebook: Using Data Science to Spot Fake News

As fake news permeates throughout social media outlets (like Facebook), it's more important than ever to identify what is fact vs. fiction. This is especially true in the age of COVID where the spread of misinformation can impact the spread of the virus. Data scientists are emerging on the front lines of this fight, using programs to help them identify fake news sources. Led by Dr. Teon Brooks, Josh Modeste, and Steven Azeka from the Gotham Data Clinic (fka CiELabs), this workshop will explore the “life cycle” of scientific research and how scientists come to consensus about how we interpret data. We’ll examine what to look for in credible news sources, and the consequences of leaning on not-so-trusty pseudoscientific sources. Then you’ll put that knowledge to work by developing a rubric for identifying fake news. No experience necessary!   https://www.eventbrite.com/e/fakebook-using-data-science-to-spot-fake-news-tickets-137833492687

Political disinformation concentrates in repeated claims across media

The prevalence and dynamics of disinformation remain contentious, partly because measures have relied on posts containing URLs from untrustworthy sources. We move beyond this by detecting disinformation in text and across posts. Using retrieval-augmented generation, we match six million Facebook, Instagram, and X posts by politicians from the United States, United Kingdom, and Germany, and two million articles from news outlets to fact-checked claims. We show that disinformation is concentrated in a small number of combinations of policy issues and parties and that just over hundred distinct claims account for around half of all disinformation across the three countries. They often appear first on a single platform, later on multiple, and subsequently in news coverage before being fact-checked. These findings suggest that disinformation is concentrated in a narrow segment of political communication, potentially creating repeated exposure that may shape the terms of public debate through both social and news media.

Disinformation and grievance narratives

Synthese - Disinformation is often defined as misleading content intended to instill false beliefs. But this definition is too narrow. We need to focus instead on its broader epistemic effects....

Let's normalize debunking each other

The spread of misinformation has become an epidemic, but information hygiene can help.

Fact-Checks Can Help Inoculate LLMs Against Disinformation

Large language models (LLMs) have become the first source millions consult when evaluating political claims, yet they were trained on an open internet that state-sponsored disinformation operations have been designed to pollute. We audit four frontier models across 2,268 responses to 63 fabrications from eight documented influence operations and find that even a single published fact-check can shift a model from hedging about a fabrication to rejecting it outright. Overall, models correctly rejected only 81% of fabrications while repeating disinformation in 3% of cases. Critically, the remaining 16% of responses would have left users unable to determine whether an fabricated claims were true or false. Fact-checks may present an avenue for addressing this vulnerability. Narratives which had been debunked by at least one IFCN certified fact-checking organization were correctly rejected as disinformation 93% of the time compared to just 76% of the time for unchecked narratives. Exploratory analyses suggest that this mechanism operates through training data, with models echoing the specific vocabulary of published corrections when available during their training windows. We conclude by discussing how fact-checks designed for human audiences appear to serve as effective protection for LLMs, converting both model uncertainty and outright disinformation into definitive rejections

The spreading of misinformation online

Significance 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 is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size. , 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.

Disinformation as Cultural Narrative: Conceptualizing Disinformation as Cross-Platform, Identity-Affirming, Cathartic Stories

Rather than framing disinformation as false facts which can be countered by true facts, we propose a model of disinformation as narrative by tracing three case studies of successful disinformation ...

Why the backfire effect does not explain the durability of political misperceptions

Previous research indicated that corrective information can sometimes provoke a so-called “backfire effect” in which respondents more strongly endorsed a misperception about a controversial political or scientific issue when their beliefs or predispositions were challenged. I show how subsequent research and media coverage seized on this finding, distorting its generality and exaggerating its role relative to other factors in explaining the durability of political misperceptions. To the contrary, an emerging research consensus finds that corrective information is typically at least somewhat effective at increasing belief accuracy when received by respondents. However, the research that I review suggests that the accuracy-increasing effects of corrective information like fact checks often do not last or accumulate; instead, they frequently seem to decay or be overwhelmed by cues from elites and the media promoting more congenial but less accurate claims. As a result, misperceptions typically persist in public opinion for years after they have been debunked. Given these realities, the primary challenge for scientific communication is not to prevent backfire effects but instead, to understand how to target corrective information better and to make it more effective. Ultimately, however, the best approach is to disrupt the formation of linkages between group identities and false claims and to reduce the flow of cues reinforcing those claims from elites and the media. Doing so will require a shift from a strategy focused on providing information to the public to one that considers the roles of intermediaries in forming and maintaining belief systems.