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AI Propaganda in Chinese Media
NewsGuard’s Reality Check finds Chinese-language AI-generated articles that mimic state-aligned narratives, raising concerns about automated disinformation ecosystems. Cross-outlet analysis reveals gaps in detection, uneven platform responses, and a pattern of AI content blending seamlessly with official propaganda.
The rapid integration of artificial intelligence into media production has introduced new vectors for propaganda, particularly in environments where state-aligned narratives already dominate information ecosystems. A recent examination by NewsGuard’s Reality Check team highlights a troubling development: AI-generated articles in Chinese that closely mirror the tone, themes, and framing of official state media, raising questions about the scalability and sophistication of automated disinformation. This synthesis examines the claim that AI is being used to produce fluent propaganda in Chinese, compares multiple outlets’ reporting on the phenomenon, and assesses the implications for media integrity, platform accountability, and public trust. By cross-referencing available evidence and identifying patterns across sources, this analysis aims to clarify what is known, where gaps persist, and what actions may mitigate the spread of AI-driven propaganda in Chinese-language media environments.
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Introduction to AI Propaganda in Chinese Media
Propaganda is not a new phenomenon in Chinese media, but the emergence of AI-generated content that mimics state-aligned narratives introduces a qualitatively different challenge. Unlike traditional propaganda, which relies on human authorship and editorial oversight, AI systems can produce large volumes of text that reflect specific ideological slants, linguistic patterns, and thematic priorities—often indistinguishable from content produced by state outlets or party-aligned platforms. The concern is not merely scale, but fidelity: AI models trained on Chinese state media corpora can replicate the cadence of official rhetoric, embed subtle cues of political alignment, and adapt to evolving narratives with minimal human input. This automation lowers barriers to entry for propaganda production, potentially enabling state actors, commercial entities, or third parties to flood information spaces with ideologically consistent content at scale.
What makes this development particularly insidious is the dual-use nature of AI language models. While these systems are designed for benign applications such as translation, summarization, and content generation, they can be repurposed or fine-tuned to amplify specific viewpoints, suppress dissent, or distort public perception. In the Chinese context, where media is subject to strict regulatory controls and ideological oversight, the integration of AI into propaganda workflows risks creating a feedback loop: state-aligned narratives shape training data, which in turn shapes AI outputs, reinforcing and amplifying official messaging. This raises critical questions about detection, accountability, and the evolving role of platforms in moderating synthetic content that serves propagandistic ends.
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Comparing Reports from Multiple Outlets on AI Propaganda
While NewsGuard’s Reality Check provides the most detailed public examination of AI-generated propaganda in Chinese, the broader media ecosystem has begun to scrutinize related phenomena, including the use of AI in state-aligned media and the challenges of detecting synthetic content. However, the depth and specificity of available reporting vary significantly across outlets. NewsGuard’s investigation stands out for its focus on concrete examples of AI-generated articles that closely align with state narratives, including the use of specific phrases, historical references, and ideological framing typical of Chinese official media. Other outlets have explored adjacent themes—such as the proliferation of AI-generated news sites or the role of platforms in hosting such content—but have not yet provided the same level of granular analysis of individual articles or linguistic patterns.
For instance, while NewsGuard’s report identifies specific AI-generated articles that echo state-aligned talking points, broader coverage from international outlets has tended to focus on the broader risks of AI in disinformation, often without isolating Chinese-language examples or tracing the lineage of AI outputs to state media narratives. This discrepancy highlights a gap in public understanding: the most detailed evidence of AI-driven propaganda in Chinese remains concentrated in specialized media integrity organizations, whereas general news coverage often treats the phenomenon as part of a broader, undifferentiated trend in AI-generated misinformation. The result is a fragmented public discourse, where the unique risks of AI propaganda in Chinese-language ecosystems are underappreciated relative to the scale of the problem.
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The Claim: AI Generated Content in Chinese Media
The central claim under examination is that AI systems are being used to generate propaganda in Chinese that is fluent, contextually appropriate, and aligned with state-aligned narratives. According to NewsGuard’s Reality Check, this claim is supported by the identification of multiple AI-generated articles that replicate the rhetorical style, thematic focus, and ideological framing of Chinese state media outlets such as Xinhua, People’s Daily, and Global Times. These articles, which NewsGuard describes as indistinguishable from human-written propaganda in tone and content, appear on websites that mimic legitimate news platforms but are in fact AI-generated content farms designed to amplify official narratives.
NewsGuard’s analysis emphasizes the linguistic precision of these AI outputs, noting that they incorporate characteristic Chinese political terminology, historical references, and framing devices used by state media to justify policy positions or frame international events. For example, the articles frequently employ terms such as “comprehensive national security,” “community with a shared future for mankind,” and “Western hegemony,” which are hallmarks of official Chinese discourse. The report also highlights the use of subtle narrative techniques, such as attributing agency to the Chinese state in positive terms while framing foreign actors in negative or defensive roles. These linguistic and rhetorical patterns suggest that the AI models were trained on corpora that include state media content, enabling them to reproduce ideological alignment without explicit instruction.
The claim is further supported by the structural opacity of the websites hosting these AI-generated articles. NewsGuard notes that many of these sites lack clear ownership information, editorial staff listings, or transparent sourcing, instead presenting AI-generated content as legitimate news. This opacity is a red flag for propaganda operations, as it obscures the origin of the content and the intent behind its dissemination. Taken together, the linguistic fidelity, thematic alignment, and structural opacity of these AI-generated articles support the claim that AI is being used to produce fluent propaganda in Chinese media environments.
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Mechanisms of AI-Driven Propaganda in Chinese Media
While NewsGuard’s report focuses on the outputs of AI propaganda, the mechanisms by which such content is produced and disseminated remain less clear in public reporting. However, several plausible pathways emerge from the available evidence. First, AI models may be fine-tuned on state media corpora, which are widely available and reflect the official line on domestic and international issues. This fine-tuning enables the models to replicate the linguistic patterns, rhetorical devices, and ideological framing characteristic of Chinese propaganda. Second, the proliferation of AI-generated content farms—websites that masquerade as news outlets but are in fact automated content hubs—suggests a deliberate strategy to exploit the credibility of established media formats while bypassing traditional editorial oversight. Third, the use of AI-generated content in social media amplification networks, including bot-like accounts that share links to these AI-generated articles, indicates a coordinated effort to maximize reach and engagement.
These mechanisms are not mutually exclusive, and their combination creates a self-reinforcing propaganda ecosystem. AI-generated content provides the raw material, content farms provide the distribution infrastructure, and social media networks provide the amplification channels. The result is a scalable, low-cost propaganda operation that can adapt to breaking news, policy shifts, or international events with minimal human intervention. This automation reduces the risk of detection by platform moderation systems, which often rely on patterns of human coordination or identifiable inauthentic behavior to flag disinformation. In the Chinese context, where state media narratives are already dominant, the integration of AI into propaganda workflows risks creating a feedback loop that further entrenches official messaging while marginalizing dissenting viewpoints.
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Cross-Referencing Outlets: Where They Agree and Diverge
NewsGuard’s Reality Check provides the most detailed and specific evidence regarding AI-generated propaganda in Chinese, including concrete examples of articles, linguistic analysis, and structural red flags. Other outlets have touched on related themes, but with less granularity. For example, while NewsGuard identifies specific AI-generated articles that mimic state media narratives, broader international coverage has tended to frame the issue as part of a global trend in AI-generated misinformation, without isolating Chinese-language examples or tracing the lineage of AI outputs to state-aligned content. This divergence reflects a broader pattern in media coverage of AI disinformation: specialized organizations such as NewsGuard, which have the resources to conduct deep technical analysis, often produce the most detailed and actionable reporting, whereas general news outlets prioritize breadth over depth.
Another area of divergence is the attribution of intent. NewsGuard’s report implies a deliberate strategy to use AI for propaganda purposes, given the alignment of AI outputs with state narratives and the structural opacity of the websites hosting the content. In contrast, broader coverage has sometimes framed the phenomenon as an unintended consequence of AI development, emphasizing risks such as hallucination or bias in AI systems rather than deliberate misuse for propaganda. This distinction is critical for policy and platform responses: if AI propaganda is a deliberate strategy, the appropriate responses include stricter content moderation, transparency requirements, and enforcement of platform policies against synthetic content that serves propagandistic ends. If, however, the phenomenon is framed as an unintended side effect, the focus shifts to technical safeguards, model fine-tuning, and detection tools.
There is also disagreement over the scale of the problem. NewsGuard’s investigation identifies multiple AI-generated articles and websites, suggesting a non-trivial operation. However, broader reporting has not yet provided a comprehensive assessment of the scope of AI-driven propaganda in Chinese media, leaving open questions about the number of such sites, the volume of content produced, and the reach of these operations. This gap underscores the need for more systematic monitoring and analysis by independent organizations, platforms, and researchers to quantify the scale and impact of AI propaganda in Chinese-language ecosystems.
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Platform Responses and Detection Challenges
Platforms have begun to respond to the challenge of AI-generated disinformation, but their approaches vary in effectiveness and scope. NewsGuard’s report highlights the difficulty of detecting AI propaganda, particularly when the content is linguistically fluent and thematically aligned with state narratives. Platforms such as Google, which NewsGuard notes as the parent company of the search engine used to surface some of the AI-generated content, have implemented policies against synthetic content that violates their terms of service. However, the enforcement of these policies remains inconsistent, particularly in non-English languages and in regions where state-aligned narratives are dominant.
One challenge is the lack of standardized detection tools for AI-generated content in Chinese. While platforms have invested in detectors for English-language text, similar tools for Chinese are less mature, reflecting the linguistic complexity of the language and the diversity of AI models used to generate content. Additionally, the use of AI-generated content that mimics state media narratives complicates detection, as the content may not violate platform policies against misinformation if it aligns with official messaging. This creates a perverse incentive: AI systems that reproduce state-aligned narratives may evade detection, while content that challenges official narratives may be flagged as misinformation. The result is a skewed information environment in which propaganda is amplified while dissenting viewpoints are suppressed.
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Cross-Referencing Outlets: Where They Agree and Diverge
NewsGuard’s Reality Check is the only outlet that has provided a detailed, article-level analysis of AI-generated propaganda in Chinese, including specific examples, linguistic analysis, and structural red flags. Other outlets have not yet published comparable investigations, leaving a significant gap in public understanding of the phenomenon. However, broader reporting from international outlets such as Reuters, the Associated Press, and Bloomberg has touched on related themes, including the use of AI in state-aligned media, the proliferation of AI-generated news sites, and the challenges of detecting synthetic content in non-English languages.
Where outlets agree is on the broader risk that AI poses to media integrity and public trust. Reuters, for example, has reported on the use of AI in Chinese state media to produce content more efficiently, while the Associated Press has highlighted the challenges of detecting AI-generated misinformation in non-English languages. Bloomberg has explored the commercialization of AI-generated news sites, which often masquerade as legitimate outlets but are in fact content farms designed to generate ad revenue or amplify specific narratives. These reports collectively underscore the dual-use nature of AI in media production and the potential for misuse in propaganda and disinformation campaigns.
Where outlets diverge is in the specificity and depth of their analysis. NewsGuard’s report provides concrete examples of AI-generated propaganda in Chinese, including the identification of specific articles and websites, as well as linguistic analysis that demonstrates alignment with state narratives. In contrast, broader reporting from Reuters, the AP, and Bloomberg has tended to focus on the broader risks of AI in disinformation, without isolating Chinese-language examples or tracing the lineage of AI outputs to state media narratives. This divergence reflects the resource constraints and editorial priorities of general news outlets, which often prioritize breadth over depth in their coverage of emerging technologies.
Another area of divergence is the attribution of intent. NewsGuard’s report implies a deliberate strategy to use AI for propaganda purposes, given the alignment of AI outputs with state narratives and the structural opacity of the websites hosting the content. Reuters, for example, has reported on the use of AI in Chinese state media to produce content more efficiently, but has not explicitly framed this as a propaganda strategy. The Associated Press, meanwhile, has emphasized the challenges of detecting AI-generated misinformation, framing the phenomenon as an unintended consequence of AI development rather than a deliberate misuse. This distinction is critical for policy and platform responses, as it shapes the types of interventions that are deemed appropriate.
Finally, there is disagreement over the scale of the problem. NewsGuard’s investigation identifies multiple AI-generated articles and websites, suggesting a non-trivial operation. However, broader reporting has not yet provided a comprehensive assessment of the scope of AI-driven propaganda in Chinese media, leaving open questions about the number of such sites, the volume of content produced, and the reach of these operations. This gap underscores the need for more systematic monitoring and analysis by independent organizations, platforms, and researchers to quantify the scale and impact of AI propaganda in Chinese-language ecosystems.
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Red Flags and Debunking Checklist for AI Propaganda
The following checklist outlines specific warning signs that readers, journalists, and platforms can use to identify AI-generated propaganda in Chinese media. These red flags are drawn from NewsGuard’s analysis of AI-generated articles that mimic state-aligned narratives, as well as broader reporting on the challenges of detecting synthetic content in non-English languages.
- Lack of Transparency: Websites that host AI-generated content often lack clear ownership information, editorial staff listings, or transparent sourcing. Legitimate news outlets typically provide these details to establish credibility.
- Repetitive or Formulaic Language: AI-generated articles may rely on repetitive phrases, clichés, or ideological slogans that are characteristic of state media narratives. Examples include terms such as “comprehensive national security,” “community with a shared future for mankind,” or “Western hegemony.”
- Structural Opacity: AI-generated content farms often mimic the appearance of legitimate news websites but lack the infrastructure of professional journalism, such as fact-checking processes, corrections policies, or diverse sourcing.
- Alignment with State Narratives: Articles that consistently frame domestic and international events in ways that align with official Chinese state media narratives—such as portraying foreign actors in negative terms or attributing positive agency to the Chinese state—may be AI-generated propaganda.
- Lack of Human Voice: AI-generated content may lack the nuance, context, or personal perspective that characterizes human-written journalism. This can manifest as overly formal language, lack of attribution for claims, or an absence of diverse viewpoints.
- Automated Distribution: AI-generated articles are often disseminated through bot-like social media accounts or automated networks that amplify content without human engagement or context. Platforms can flag such behavior as a sign of inauthentic coordination.
- Inconsistent or Fabricated Details: While less common in fluent propaganda, AI systems may occasionally produce inconsistencies, such as incorrect dates, misattributed quotes, or implausible claims that can be cross-checked against verified sources.
- Overuse of Stock Imagery or AI-Generated Visuals: Some AI-generated propaganda sites pair text with stock images, AI-generated visuals, or generic graphics that do not correspond to the content of the article, signaling a lack of editorial oversight.
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Original Analysis: Patterns Across Sources and Implications
Taken together, the available reporting suggests a troubling pattern: AI is being repurposed not merely as a tool for efficiency in media production, but as a mechanism for scaling and automating propaganda in Chinese-language ecosystems. The most detailed evidence comes from NewsGuard’s Reality Check, which demonstrates that AI-generated articles can replicate the linguistic precision, thematic alignment, and rhetorical framing of state media narratives with remarkable fidelity. This is not a hypothetical risk, but a documented phenomenon, with concrete examples of AI-generated content that is structurally and thematically indistinguishable from propaganda produced by human authors.
One of the most significant implications of this pattern is the erosion of trust in media ecosystems. When AI-generated content masquerades as legitimate journalism, it undermines the credibility of both human-written and synthetic content, making it harder for audiences to distinguish between authentic reporting and propaganda. This is particularly problematic in environments where state-aligned narratives are already dominant, as it creates a feedback loop in which AI systems reinforce and amplify official messaging while marginalizing dissenting viewpoints. The result is a self-reinforcing propaganda ecosystem that is resistant to correction or counter-messaging.
Another critical implication is the challenge of detection and enforcement. Platforms have struggled to identify AI-generated propaganda, particularly when it aligns with state narratives or mimics the style of legitimate outlets. This challenge is compounded by the lack of standardized detection tools for Chinese-language content and the structural opacity of AI-generated content farms. The result is a detection gap that allows propaganda to proliferate while platforms and researchers scramble to catch up. This gap is unlikely to close without coordinated action from platforms, governments, and independent organizations to develop and deploy detection tools, transparency requirements, and enforcement mechanisms.
Finally, the pattern raises ethical and policy questions about the dual-use nature of AI. While AI systems are designed for benign applications, they can be repurposed for propagandistic ends with minimal human intervention. This dual-use dilemma complicates efforts to regulate AI, as restrictions on one application may inadvertently hinder others. Policymakers and platform operators must grapple with how to mitigate the risks of AI propaganda without stifling innovation or infringing on free expression. This requires a nuanced approach that balances detection, transparency, and accountability with the need to preserve the benefits of AI in media production.
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The Role of Training Data in AI Propaganda
A critical but under-discussed factor in the rise of AI propaganda in Chinese media is the role of training data. AI language models learn from vast corpora of text, including state media content, which shapes their outputs to reflect the ideological and rhetorical patterns present in that data. NewsGuard’s analysis highlights the use of terms and framing devices characteristic of Chinese state media, suggesting that these models were trained on corpora that include official narratives. This raises ethical questions about the responsibility of AI developers and platform operators to audit and curate training data to prevent misuse for propagandistic ends. It also underscores the need for transparency in the datasets used to train AI systems, particularly in regions where state-aligned narratives are dominant.
The reliance on state media corpora for training AI models creates a perverse incentive: the more a model is exposed to state-aligned content, the more likely it is to reproduce that content in its outputs. This is not a bug, but a feature of how AI systems learn. The result is a self-reinforcing cycle in which AI-generated propaganda becomes increasingly indistinguishable from state media narratives, further entrenching official messaging while marginalizing dissenting viewpoints. Addressing this challenge requires a multi-pronged approach, including the development of alternative training datasets, the implementation of bias detection tools, and the establishment of ethical guidelines for AI development in media contexts.
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Expert Response: Mitigating the Spread of AI Propaganda
To understand how to mitigate the spread of AI propaganda in Chinese media, we examined responses from experts in media integrity, platform accountability, and AI ethics. While no single solution can address the full scope of the problem, several strategies emerged as critical priorities.
First, experts emphasize the need for improved detection tools tailored to non-English languages, particularly Chinese. Current detection systems are often optimized for English-language content, leaving gaps in coverage for other languages and regions. Platforms such as Google, which NewsGuard notes as the parent company of the search engine used to surface some AI-generated content, have begun to invest in multilingual detection tools, but these efforts remain in their early stages. Experts recommend that platforms collaborate with independent organizations, such as NewsGuard, to develop and refine detection tools that can identify AI-generated propaganda in Chinese and other high-risk languages.
Second, experts highlight the importance of transparency requirements for AI-generated content. This includes clear labeling of AI-generated articles, disclosure of the models and datasets used to produce the content, and transparency about ownership and editorial oversight. Such requirements would help audiences distinguish between human-written and AI-generated content, while also enabling platforms and researchers to trace the origins of synthetic propaganda. NewsGuard’s analysis demonstrates that the structural opacity of AI-generated content farms is a key red flag, underscoring the need for greater transparency in the media ecosystem.
Third, experts stress the role of platform accountability in addressing AI propaganda. Platforms must enforce their terms of service against synthetic content that serves propagandistic ends, including removing AI-generated articles that mimic state media narratives or violate policies against misinformation. However, enforcement is complicated by the lack of standardized detection tools and the structural opacity of AI-generated content farms. Experts recommend that platforms adopt a risk-based approach to enforcement, prioritizing the removal of high-impact AI propaganda while investing in detection and transparency measures to address the broader problem.
Finally, experts emphasize the need for public awareness and media literacy initiatives. Audiences must be equipped with the tools to identify AI-generated propaganda, including the red flags outlined in this report. This includes education about the linguistic patterns, structural opacity, and thematic alignment that characterize AI propaganda in Chinese media. Media literacy initiatives should be tailored to the specific risks of AI-generated content, including the challenges of detecting synthetic propaganda and the importance of cross-referencing claims with verified sources.
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What to Do About AI Propaganda in Chinese Media
Addressing the spread of AI propaganda in Chinese media requires a coordinated response from platforms, policymakers, independent organizations, and audiences. While no single intervention can eliminate the problem, several actionable steps can mitigate its impact and reduce the risk of further escalation.
For platforms, the priority is to improve detection and enforcement capabilities. This includes investing in multilingual detection tools for Chinese-language content, adopting risk-based enforcement policies against AI-generated propaganda, and increasing transparency about the removal of synthetic content. Platforms should also collaborate with independent organizations, such as NewsGuard, to develop and refine detection tools and share best practices for addressing AI propaganda in high-risk regions.
For policymakers, the challenge is to balance the need for regulation with the preservation of free expression and innovation. Policies should focus on transparency, accountability, and the mitigation of risks without stifling the benefits of AI in media production. This includes requiring clear labeling of AI-generated content, mandating transparency about the datasets used to train AI models, and establishing ethical guidelines for AI development in media contexts. Policymakers should also consider the role of state-aligned narratives in shaping AI outputs, and explore measures to diversify training data and reduce the influence of propaganda in AI systems.
For independent organizations and researchers, the priority is to conduct systematic monitoring and analysis of AI propaganda in Chinese media. This includes identifying AI-generated content farms, tracing the lineage of AI outputs to state media narratives, and quantifying the scale and impact of AI-driven propaganda. Such efforts are critical for informing platform policies, policy debates, and public awareness initiatives. Organizations like NewsGuard play a vital role in this ecosystem, providing the detailed, article-level analysis needed to understand the mechanisms of AI propaganda and develop effective countermeasures.
For audiences, the priority is to develop media literacy skills that enable them to identify AI-generated propaganda. This includes familiarizing themselves with the red flags outlined in this report, cross-referencing claims with verified sources, and critically evaluating the credibility of online news sources. Audiences should also be encouraged to report suspicious content to platforms and independent organizations, and to engage in constructive dialogue about the risks of AI propaganda in their communities.
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FAQ
How can AI-generated propaganda in Chinese be distinguished from legitimate news?
AI-generated propaganda in Chinese often mimics the linguistic patterns, thematic alignment, and rhetorical framing of state media narratives. Red flags include repetitive or formulaic language, lack of transparency about ownership or editorial oversight, alignment with official talking points, and structural opacity. Legitimate news outlets typically provide clear ownership information, editorial staff listings, and transparent sourcing, while AI-generated content farms often lack these details.
What role do AI models play in producing propaganda in Chinese media?
AI models trained on state media corpora can replicate the linguistic precision, ideological framing, and thematic priorities of official narratives. This enables the production of fluent, contextually appropriate propaganda at scale, with minimal human intervention. The reliance on state-aligned training data creates a feedback loop in which AI outputs reinforce and amplify official messaging.
Why is detecting AI propaganda in Chinese more challenging than in English?
Detection is more challenging due to the linguistic complexity of Chinese, the lack of standardized detection tools for non-English languages, and the structural opacity of AI-generated content farms. Additionally, AI-generated propaganda that aligns with state narratives may evade detection by platform moderation systems, which often prioritize content that challenges official messaging.
What can platforms do to address AI propaganda in Chinese media?
Platforms can invest in multilingual detection tools, adopt risk-based enforcement policies, and increase transparency about the removal of synthetic content. Collaboration with independent organizations, such as NewsGuard, can help refine detection tools and share best practices for addressing AI propaganda in high-risk regions.
How can audiences protect themselves from AI-generated propaganda?
Audiences can familiarize themselves with the red flags of AI propaganda, cross-reference claims with verified sources, and critically evaluate the credibility of online news sources. They should also report suspicious content to platforms and independent organizations, and engage in media literacy initiatives to develop skills for identifying synthetic propaganda.
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