How Influence Campaigns Actually Work in 2025
A Late-2025 Field Model With Implications for 2026
Written December 2025
This paper may receive minor updates for clarity or additional references. The analysis reflects the influence ecosystem as of December 2025.
Public discussions of influence and propaganda often lag reality. This paper offers a practical model for understanding how influence campaigns actually operate in late 2025. It aims to describe the mechanisms shaping modern influence and to explain why many traditional defenses feel misaligned or ineffective.
Contents
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Introduction​
Public discussions of influence and propaganda often lag reality. They focus on content such as false claims, viral posts, or misleading narratives, while overlooking the systems that determine what information becomes salient, credible, and actionable in the first place.
This paper offers a practical model for understanding how influence campaigns actually operate in late 2025. It synthesizes open reporting, platform behavior, and observable patterns across social media, AI systems, and institutional response. The goal is not prediction or alarm, but clarity. It aims to describe the mechanisms shaping modern influence and to explain why many traditional defenses feel misaligned or ineffective.
The analysis is structured around four core components:
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Influence campaigns as operational systems
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Fog generation as a dominant strategy
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The emergence of the AI answer layer
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Strategic implications moving into 2026
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Methodological Note
This paper is a synthesis of open-source reporting, platform transparency disclosures, academic research, and observed patterns across influence campaigns rather than a presentation of original empirical data. It is intended as a field model, not a definitive taxonomy.
Executive Summary
In 2025, effective influence campaigns are best understood not as isolated pieces of disinformation, but as operational systems designed to shape the informational environment in which people think, decide, and act. Their success depends less on persuading audiences of falsehoods and more on controlling salience, credibility pathways, and emotional tempo across platforms.
While generative AI has lowered the cost of producing and adapting content by enabling rapid variation, localization, and tone matching, it has not fundamentally changed the core constraints of influence operations. Distribution, legitimacy, timing, and audience trust remain decisive. As a result, modern campaigns focus on building and leveraging infrastructure such as persona networks, seemingly authentic outlets, cross platform presence, and amplification pathways that make narratives feel organic rather than imposed.
A defining characteristic of contemporary influence efforts is that most content is not demonstrably false. Instead, campaigns rely on selective framing, emotional priming, repetition, and agenda setting to subtly redefine what feels normal, reasonable, or risky. The objective is often environmental rather than ideological. Campaigns seek to shape what topics dominate attention, narrow the range of acceptable discourse, or increase cynicism and disengagement rather than win overt agreement.
Influence campaigns increasingly operate across the full internet ecosystem rather than single platforms. They blend social media activity with owned websites, media outreach, and opportunistic engagement with existing communities. In mature phases, campaigns may leverage established influencers through direct sponsorship, access incentives, or simple narrative alignment. In many cases, those involved do not experience this as participation in an influence operation at all, but as ordinary commentary or coverage.
A critical shift in recent years is the growing importance of fog generation as a strategy. During fast moving events, campaigns aim to increase uncertainty, misidentification, and narrative churn faster than institutions, platforms, or crowds can stabilize shared understanding. This approach exploits the latency of corrective mechanisms and the economics of attention, where initial impressions often outpace later clarification.
Finally, a new and under examined influence surface has emerged: the AI answer layer. As more people rely on AI assistants to summarize news and explain events, inaccuracies, sourcing failures, and confident synthesis errors can unintentionally distort narratives at scale. Influence no longer targets only what people see, but what their tools confidently tell them is true.
Taken together, these dynamics explain why content centric defenses such as fact checking, takedowns, and moderation alone struggle to keep pace. Influence campaigns do not primarily win by spreading lies. They win by reshaping the terrain on which truth, trust, and decision making operate.
The Influence Campaign Lifecycle (2025)
Classical models of propaganda emphasize messaging such as slogans, speeches, and repeated claims designed to persuade mass audiences. While those techniques still exist, they are no longer sufficient to explain how influence works in a fragmented, algorithmic, and credibility constrained environment.
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In 2025, influence campaigns function less like broadcast persuasion and more like iterative operational systems. They are designed to probe, adapt, and exploit the informational ecosystem over time. While no two campaigns are identical, effective efforts tend to follow a common lifecycle.
Understanding this lifecycle is essential because most defensive approaches target visible outputs rather than the underlying process.
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1. Objective Definition: Shaping the Environment, Not Just Beliefs
Every influence campaign begins with a defined objective, but that objective is rarely to convince everyone of a specific claim.
More common goals include:
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Making a topic feel omnipresent or unavoidable
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Normalizing a previously marginal frame
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Increasing cynicism, disengagement, or resignation
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Creating perceived risk around speaking or acting
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Forcing institutions into credibility damaging responses
Success is measured less by belief conversion and more by changes in the decision environment. This includes what feels safe to say, what feels controversial, what feels futile, and what feels inevitable.
This represents a major departure from classical propaganda models.
2. Infrastructure and Identity Formation: Credibility Before Content
Before meaningful messaging begins, campaigns invest in credibility pathways.
This phase focuses on:
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Creating accounts, pages, channels, and domains
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Establishing personas such as individuals, outlets, or movements
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Developing consistent visual and linguistic identity
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Building cross platform presence
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Allowing assets to age without controversy
At this stage, content is often banal, neutral, or uninteresting. The goal is not influence yet, but plausibility.
In modern ecosystems, legitimacy is a prerequisite for reach. Infrastructure enables future amplification to appear organic rather than orchestrated.
3. Content Pipeline Construction: Framing, Not Fabrication
Only after infrastructure exists does content become central.
Contrary to popular perception, most campaign content is:
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Factually mixed or largely true
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Selective rather than fabricated
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Framed to evoke emotion or identity
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Designed to set agendas rather than argue positions
In 2025, generative tools are commonly used to rapidly produce variations of the same idea, match platform native tone and style, localize language and cultural references, and maintain posting cadence at scale.
The advantage of AI in this phase is efficiency and adaptability, not persuasion magic. It allows campaigns to experiment cheaply and continuously.
4. Distribution Testing: Probing for Traction
Before large scale amplification, campaigns test narratives.
This involves posting across multiple platforms and communities, varying tone and emotional triggers, monitoring engagement velocity and resistance, and identifying which narratives spread and which stall.
Most narratives fail at this stage and are quietly discarded. This is why influence campaigns often appear inconsistent or contradictory when viewed in isolation.
Distribution testing turns influence into an adaptive process rather than a fixed script.
5. Amplification Pathways: Scaling Legitimacy
Once a narrative demonstrates traction, amplification becomes the priority.
This can occur through coordinated network activity, algorithmic exploitation, opportunistic media pickup, and engagement by established influencers.
Influencer amplification often occurs through direct sponsorship, access based incentives, narrative alignment, or social proof effects. In many cases, those amplifying the message do not perceive themselves as participating in an influence operation at all.
This stage is where campaigns acquire borrowed credibility, not just reach.
6. Measurement and Feedback: Reading the Environment
Campaigns continuously monitor engagement patterns, cross platform narrative pickup, media echo effects, institutional responses, and counter framing.
The key question is whether a narrative has moved from content to ambient assumption, meaning something people reference without attribution or debate.
At this point, influence has succeeded even if no one explicitly agrees with the original source.
7. Adaptation, Dormancy, or Rebranding
When resistance emerges or conditions change, campaigns adapt. They may soften narratives, pivot messengers, let assets go dormant, or reuse infrastructure for new objectives.
Successful campaigns rarely collapse outright. They evolve, often becoming harder to attribute because earlier phases established plausible independence.
Fog as Strategy: Winning Without Persuasion
One of the most consequential shifts in modern influence campaigns is the move away from persuasion as the primary objective. In many contemporary operations, the goal is not to convince audiences of a particular claim, but to degrade the informational environment itself.
This approach can be described as fog generation.
Rather than pushing a single narrative to dominance, fog based strategies aim to increase uncertainty, contradiction, emotional volatility, and narrative churn faster than institutions, platforms, or communities can stabilize shared understanding.
In fog dominated environments, influence does not require agreement. It only requires hesitation.
Fog vs Persuasion
Classical propaganda assumes a contest between messages where one side persuades more effectively than the other. Fog strategies operate differently.
They seek to delay sense making, undermine confidence in authoritative accounts, create competing explanations that feel equally plausible, exhaust attention and emotional bandwidth, and encourage disengagement.
The desired outcome is not belief in a falsehood, but loss of orientation.
Why Fog Works in 2025
Fog strategies are particularly effective due to speed asymmetry, attention economics, cognitive load limits, and fragmented trust. False or speculative content spreads faster than correction, outrage outperforms nuance, and no single authority commands universal credibility.
Fog exploits these conditions without requiring narrative coherence. Internal inconsistency can be an asset.
Fog as a Lifecycle Strategy
Fog generation is most effective during distribution testing, amplification surges, institutional response windows, and adaptation phases. Confusion masks strategic pivots and complicates attribution.
The Strategic Payoff
When fog succeeds, audiences disengage, institutions appear confused, corrections are reframed as spin, influencers hesitate, and trust erosion persists even after facts are established.
Influence succeeds without persuading anyone of anything specific. The terrain itself is reshaped.
The AI Answer Layer: Influence at the Point of Synthesis
A new influence surface has emerged that fundamentally changes how narratives form and spread: the AI answer layer.
AI assistants increasingly mediate how people access information by summarizing news, answering questions, and synthesizing sources. This layer does not merely transmit information. It constructs meaning.
Influence at this layer is quieter, more authoritative, and often invisible.
From Feeds to Answers
Traditional influence campaigns compete for attention. The AI answer layer shifts the target to questions, framing, summaries, source selection, and uncertainty smoothing.
When an AI assistant produces a confident answer, that answer often becomes the user’s default mental model, even if the underlying information is incomplete or contested.
Structural Vulnerabilities
This layer introduces compression pressure, authority transfer, source opacity, and confidence bias. Interpretive errors can scale without virality.
Influence Without Content Creation
Influence at this layer does not require viral content. Campaigns can seed consistent framings across many low visibility sources. When AI systems synthesize across them, narratives emerge organically. This is influence by statistical gravity.
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Fog Meets Synthesis
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Fog strategies compound here. AI systems may average conflicting accounts, present false balance, collapse timelines, or smooth over disputes. The result is ambiguous authority rather than falsehood.
Strategic Implications
Influence must be treated as a systems risk, not a content problem.
Key implications include:
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Latency is the core vulnerability
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Credibility chokepoints matter more than reach
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AI systems are part of the information supply chain
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Institutional humility preserves trust
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Individual resilience is insufficient
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Effective defense will look quiet
2026 Outlook: What Changes, What Does Not
The dynamics described here are unlikely to reverse in 2026, but several trends will intensify.
Influence will shift further upstream toward infrastructure and synthesis layers. AI mediation will normalize, making epistemic risk a governance concern. Fog will outperform persuasion during crises. Attribution will matter less strategically. Defense will become more institutional and less individual.
The core challenge of 2026 will not be detecting falsehoods, but maintaining coherent sense making under pressure.
Closing
Influence campaigns in 2025 do not primarily attack truth. They attack the processes by which truth is resolved.
Defending against them requires moving upstream from content to systems, from persuasion to interpretation, and from reaction to anticipation.
Overall, the most dangerous influence campaigns are not those that change minds, but those that quietly redefine what thinking feels like.
References
Meta Platforms, Inc. (n.d.). Threat reporting (adversarial threat reports, CIB, threat disruptions). Meta Transparency Center.
https://transparency.meta.com/metasecurity/threat-reporting/
Meta Platforms, Inc. (2025). Integrity reports: First quarter 2025. Meta Transparency Center.
https://transparency.meta.com/reports/integrity-reports-q1-2025/
Microsoft Threat Analysis Center (MTAC). (2024, September 17). Russian election interference efforts focus on the Harris-Walz campaign. Microsoft On the Issues.
Microsoft. (2025). Microsoft Digital Defense Report 2025.
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OpenAI. (2025, June). Disrupting malicious uses of AI: June 2025. OpenAI (Global Affairs).
https://openai.com/global-affairs/disrupting-malicious-uses-of-ai-june-2025/
OpenAI. (2025, October). Disrupting malicious uses of AI: October 2025. OpenAI (Global Affairs).
https://openai.com/global-affairs/disrupting-malicious-uses-of-ai-october-2025/
Reuters. (2025, October 21). AI assistants make widespread errors about the news, new research shows. Reuters.
The Verge. (2025, February 11). AI chatbots are distorting news stories, BBC finds. The Verge.
https://www.theverge.com/news/610006/ai-chatbots-distorting-news-bbc-study
Slaughter, I., Peytavin, A., Ugander, J., & Saveski, M. (2025). Community Notes Moderate Engagement With and Diffusion of False Information Online (arXiv:2502.13322).
https://arxiv.org/abs/2502.13322
Slaughter, I., Peytavin, A., Ugander, J., & Saveski, M. (2025). Community notes reduce engagement with and diffusion of false information online. Proceedings of the National Academy of Sciences, 122(38), e2503413122.
https://doi.org/10.1073/pnas.2503413122
Renault, T., Restrepo Amariles, D., & Troussel, A. (2024). Collaboratively adding context to social media posts reduces the sharing of false news (arXiv:2404.02803).
https://arxiv.org/abs/2404.02803
YaleNews. (2025, September 25). Flagging misinformation on social media reduces engagement, study finds. YaleNews.
https://news.yale.edu/2025/09/25/flagging-misinformation-social-media-reduces-engagement-study-finds
European Parliament Research Service (EPRS). (2025, December). Information manipulation in the age of generative artificial intelligence (Briefing).
https://www.europarl.europa.eu/thinktank/en/document/EPRS_BRI%282025%29779259
Further reading
Benkler, Y., Faris, R., & Roberts, H. (2018). Network propaganda: Manipulation, disinformation, and radicalization in American politics. Oxford University Press.
https://global.oup.com/academic/product/network-propaganda-9780190923624
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Bradshaw, S., & Howard, P. N. (2019). The global disinformation order: 2019 global inventory of organised social media manipulation (Working Paper 2019.2). Oxford Internet Institute / Project on Computational Propaganda.
https://digitalcommons.unl.edu/scholcom/207/
Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51–58.
https://academic.oup.com/joc/article/43/4/51/4105637
Starbird, K. (2019). Disinformation’s spread: bots, trolls and all of us. Nature, 571, 449.
https://doi.org/10.1038/d41586-019-02235-x
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Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe.
