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New preprint: Political Disinformation: ‘Fake News’, Bots, and Deep Fakes

I’m happy to share that my preprint Political Disinformation: ‘Fake News’, Bots, and Deep Fakes is now available. It will appear in the *Oxford Research Encyclopedia of Communication* sometime in the future.

You can read the preprint here: Political Disinformation: ‘Fake News’, Bots, and Deep Fakes

The piece is an attempt to take a step back from the often alarmist public discourse on disinformation and look more carefully at what we actually know. Concerns about political disinformation—whether in the form of fake news, bots, deepfakes, or foreign interference—have become increasingly prominent, especially since 2016. But these concerns don’t always align with what empirical research tells us.

In the article, I try to do three things:

  • Clarify what we’re talking about: I revisit some of the definitional challenges around disinformation and related terms. A central point is that political speech is often contested, interpretive, and not easily reduced to true vs. false categories.
  • Summarize the current evidence: Drawing on a broad range of studies, I look at what we know about the actual reach and effects of disinformation. While the issue is real, the available data suggest that its impact is more limited than many fear.
  • Reflect on how we respond: Efforts to fight disinformation can come with their own risks, especially if they concentrate power over speech or suppress disagreement. I argue for responses that are proportionate, evidence-based, and attentive to democratic openness.
  • This piece is meant as a contribution to ongoing conversations across research, policy, and civil society. It doesn’t dismiss the challenges posed by disinformation, but it does suggest we might better address them by being more precise in our terms, more rigorous in our methods, and more careful about the trade-offs involved.

    Abstract: Political disinformation has become a central concern in both public discourse and scholarly inquiry, particularly following the electoral successes of right-wing populist movements in Western democracies since 2016. These events have been widely interpreted through the lens of manipulation, deviance, and disruption; all closely linked to the spread of false information, automated amplification, and foreign interference in digital communication environments. More recently, advances in Artificial Intelligence have added to anxieties about the growing sophistication and scale of disinformation campaigns. Collectively, these concerns are often framed under the concept of digital disinformation and viewed as posing existential threats to democratic systems. This entry provides a comprehensive and critical overview of the disinformation debate. It traces the definitional and conceptual challenges inherent in the term “disinformation,” highlights how digital infrastructures shape both the problem and its perceived urgency, and synthesizes empirical evidence on the actual reach, distribution, and impact of political disinformation. The article distinguishes between individual, collective, and discursive harms, while cautioning against inflated threat narratives that outpace empirical findings. Importantly, the entry addresses the risks of regulatory overreach and centralized control. Efforts to counter disinformation may themselves undermine democratic openness, suppress dissent, and weaken societies’ capacity for collective information processing. In response, the article outlines a research agenda that prioritizes conceptual clarity, empirical rigor, and systemic analysis over alarmism. It advocates for a shift away from overstreching framings of disinformation toward more precise and differentiated understandings of digital political communication and its challenges.

  • Andreas Jungherr (2025). Political Disinformation: “Fake News”, Bots, and Deep Fakes. In Oxford Research Encyclopedia of Communication. Oxford: Oxford University Press. (Forthcoming).
  • New working paper: What do people expect from Artificial Intelligence? Public opinion on alignment in AI moderation from Germany and the United States

    With the rapid adoption of generative AI tools like ChatGPT and Midjourney, societies are increasingly grappling with questions of control, trust, and responsibility. As AI systems become more integrated into everyday life, public expectations around how these systems are governed – and for what purposes – take on growing importance.

    In our new working paper, we investigate a central question in AI ethics and policy:

    What do people expect from moderation in AI-enabled systems, and how do these expectations vary across countries?

    To answer this, we developed a new framework for the conceptualization of different moderation approaches and for the explanation of their demand or opposition. To test this framework, we ran two large-scale surveys – one in the United States (n = 1,756) and one in Germany (n = 1,800). We asked respondents to evaluate four core goals of AI output moderation:

    • Accuracy and Reliability: Does the AI provide factual and trustworthy content?
    • Safety: Does it avoid producing harmful or illegal outputs?
    • Bias Mitigation: Are efforts made to reduce unfairness in its responses?
    • Aspirational Imaginaries: Does the AI help envision a better, more inclusive society?

    Key Findings at a Glance

    Broad support for accuracy and safety

    These two goals enjoy the strongest backing across both countries. The public clearly wants AI systems that are factually reliable and prevent harm.

    Mixed support for fairness and aspirational goals

    Support for interventions to mitigate bias or promote idealistic visions of society is more cautious—especially in Germany.

    National differences in preferences

    U.S. respondents are more open to AI interventions across the board, reflecting greater familiarity with the technology and a more innovation-oriented culture. German respondents, by contrast, are more skeptical and differentiate more between moderation goals.

    What explains these differences?

    We propose a three-level model of “involvement” that shapes attitudes toward AI:

    • Individual-level: AI experience and free speech values
    • Group-level: Gender and political affiliation
    • System-level: The broader national context (U.S. as high-involvement, Germany as low-involvement)

    In the U.S., where AI is more widely used and publicly debated, expectations are more consistent and ideologically structured. In Germany, individual values and experience with AI play a bigger role in shaping attitudes.

    Why This Matters for AI Governance

    Our findings suggest that public support for AI regulation is goal-dependent. People are not simply “for” or “against” moderation — they care about why an intervention is being made.

    This has clear implications for policymakers and developers:

    • Don’t assume consensus. The public holds nuanced, context-sensitive views.
    • Communicate clearly. Explain not just how AI systems work, but what they are designed to achieve.
    • Build trust through transparency. Especially in low-exposure contexts like Germany, trust depends on open communication and user engagement.

    Read the full paper

    What do people expect from Artificial Intelligence? Public opinion on alignment in AI moderation from Germany and the United States
    By Andreas Jungherr & Adrian Rauchfleisch

    Abstract:

    Recent advances in generative Artificial Intelligence have raised public awareness, shaping expectations and concerns about their societal implications. Central to these debates is the question of AI alignment — how well AI systems meet public expectations regarding safety, fairness, and social values. However, little is known about what people expect from AI-enabled systems and how these expectations differ across national contexts. We present evidence from two surveys of public preferences for key functional features of AI-enabled systems in Germany (n = 1800) and the United States (n = 1756). We examine support for four types of alignment in AI moderation: accuracy and reliability, safety, bias mitigation, and the promotion of aspirational imaginaries. U.S. respondents report significantly higher AI use and consistently greater support for all alignment features, reflecting broader technological openness and higher societal involvement with AI. In both countries, accuracy and safety enjoy the strongest support, while more normatively charged goals — like fairness and aspirational imaginaries — receive more cautious backing, particularly in Germany. We also explore how individual experience with AI, attitudes toward free speech, political ideology, partisan affiliation, and gender shape these preferences. AI use and free speech support explain more variation in Germany. In contrast, U.S. responses show greater attitudinal uniformity, suggesting that higher exposure to AI may consolidate public expectations. These findings contribute to debates on AI governance and cross-national variation in public preferences. More broadly, our study demonstrates the value of empirically grounding AI alignment debates in public attitudes and of explicitly developing normatively grounded expectations into theoretical and policy discussions on the governance of AI-generated content.

    Read the full paper on arXiv

    New Course: Misinformation, disinformation and other digital fakery (Summer 2025)

    Misinformation, disinformation, and other forms of digital deception have become central concerns in both academic inquiry and public debate. News outlets regularly spotlight incidents of disinformation; political actors accuse each other of spreading falsehoods; and regulatory initiatives often cite the threat of digital disinformation to justify increased oversight of communication environments. However, effective regulation requires a careful balance between mitigating these threats and safeguarding democratic freedoms.

    Addressing this challenge demands an empirically grounded understanding of the reach, effects, and mechanisms of digital disinformation. The social sciences play a vital role in developing concepts and methods to reliably identify, measure, and analyze these phenomena.

    This course equips students with a robust conceptual toolkit to critically engage with core issues surrounding misinformation, disinformation, and digital fakery. Through structured readings, presentations, and guided research projects, students will explore the actors, strategies, and effects associated with digital disinformation and develop their own research inquiries in the field.

    For a comprehensive outline of weekly topics and assigned readings, please refer to the full syllabus here.

    New Course: Artificial Intelligence and Democracy (Summer 2025)

    As Artificial Intelligence (AI) technologies advance, they increasingly shape how democratic and autocratic governments operate, how political actors communicate, and how citizens engage with the public sphere. This seminar offers an in-depth and interdisciplinary examination of the relationship between AI and democracy, combining foundational knowledge of AI systems with cutting-edge research on their political and societal implications.

    The course begins with an introduction to core concepts: What is AI? How does it work? And under what conditions can it be effectively and safely applied? Building on this technical and conceptual groundwork, we explore how AI affects democratic institutions and processes—such as public discourse, elections, political communication, and regulatory governance—as well as how AI technologies are used by autocratic regimes for control and surveillance.

    Weekly sessions are structured around thematic case studies and comparative readings that examine topics such as:

  • Foundations of AI and its alignment with human values
  • Regulatory efforts and the politics of AI governance
  • The use of AI in public administration and service delivery
  • AI’s role in journalism, public debate, and misinformation
    Campaigning, microtargeting, and opinion shaping through AI
  • The strategic deployment of AI in authoritarian regimes
  • Students will engage with contemporary scholarly debates through active discussion, critical reading, and presentations based on empirical research and theoretical analysis. The seminar draws on examples from across democratic and autocratic systems, with an emphasis on how AI technologies interact with broader processes of digital transformation, institutional change, and value contestation.

    For a comprehensive outline of weekly topics and assigned readings, please refer to the full syllabus here.

    New Working Paper: “Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide”

    We are excited to announce the release of our new working paper, now available on arXiv. In this new working paper, Adrian Rauchfleisch and I continue our exploration of people’s opinions on Artificial Intelligence (AI) in democracy and politics. After an earlier study on the public’s views on AI in campaigning, we now turn to public opinion on AI in deliberation:

    Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide

    Digital deliberation significantly expands opportunities for democratic engagement, yet managing deliberative processes at scale remains challenging. While AI promises to enhance efficiency in digital deliberation, our findings reveal a noteworthy “AI penalty”: public skepticism towards AI in deliberative contexts.

    Through a preregistered survey experiment with a representative sample in Germany (n=1850), we discovered that participants exhibit less willingness to engage in AI-facilitated deliberations compared to human-led formats and rate their quality significantly lower. Crucially, attitudes towards AI—including perceived benefits, risks, and the extent of anthropomorphization—significantly moderate these effects.

    Our study highlights the emergence of a new deliberative divide driven by individual attitudes towards AI. This divide presents a novel challenge to democratic participation, distinct from traditional divides shaped by demographics or educational background. As democratic practices increasingly move online and leverage AI, understanding and addressing public perceptions and hesitancy toward AI will be critical.

    Read our full working paper here: https://arxiv.org/abs/2503.07690

    Andreas Jungherr, and Adrian Rauchfleisch. 2025. Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide. arxiv. Working Paper. doi: 10.48550/arXiv.2503.07690

    Interview: Bundestagswahl 2025 – Der Tag danach

    Mit Andreas Bachmann habe ich im BR das Wahlergebnis kommentiert und die Pressekonferenzen der Parteien begleitet.

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