Synthetic Persona and Disinformation Swarm Detection: Evidence Attribution and False Positives

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The proliferation of autonomous artificial intelligence, large language models (LLMs), and hyper-connected social ecosystems has fundamentally altered the threat landscape of digital information. As state-sponsored actors, commercial public relations (PR) firms, and decentralized threat networks adapt to platform defenses, the phenomenon of Coordinated Inauthentic Behavior (CIB) has evolved into complex, multi-layered Foreign Information Manipulation and Interference (FIMI) campaigns1. The deployment of synthetic personas and automated disinformation swarms presents an unprecedented challenge for digital forensics, open-source intelligence (OSINT), and platform governance. Identifying these campaigns requires distinguishing highly sophisticated, AI-assisted hostile information operations from ordinary automation, pseudonymous human interaction, synchronized civic activism, and organic community engagement3. As threat actors utilize commercialized "fake-engagement-as-a-service" models and weaponize legitimate open-source cybersecurity frameworks, attribution methodologies must transcend simple content analysis5. The reliance on statistical anomalies, network clustering, and structural predictability offers a pathway to detection. However, the aggressive application of these defensive mechanisms carries a severe risk of false positives. Such errors disproportionately penalize vulnerable internet users, including emergency-response communities, non-native speakers, neurodivergent writers, role-players, and anonymous activists operating in hostile jurisdictions7. This analysis details the rigorous frameworks required to attribute FIMI operations, dissects high-level defensive evidence, surveys critical international case studies, and explores the profound consequences of algorithmic bias in synthetic content detection.

Categorical Separations in Disinformation Architecture

To accurately attribute and disrupt a disinformation swarm, threat intelligence frameworks must isolate the distinct operational layers of an influence campaign. Conflating the mechanism of a campaign with its origin or ultimate purpose inevitably leads to flawed attribution and ineffective countermeasures10. The architecture of digital influence must be separated into the following sequential components.

Content Generation vs. Coordination

Content generation refers to the mechanisms used to create digital artifacts, whether handwritten articles, deepfake imagery, or LLM-generated propaganda11. The use of synthetic generation is not inherently malicious; it represents a technological capability that exists on a spectrum from benign creative uses to heavily weaponized outputs, sometimes referred to conceptually as an "AI Slop Funnel"11. Coordination, however, is the behavioral mechanism of distribution. It involves the synchronized, automated, or orchestrated dissemination of that content across network nodes to manufacture the illusion of organic consensus, a practice widely documented as astroturfing1. An actor may use purely authentic, human-generated news articles but distribute them through a highly coordinated, inauthentic botnet. Conversely, an actor might use AI to generate low-quality synthetic content but post it manually from a single account11. Coordination is the primary indicator of CIB, independent of how the underlying content was synthesized.

Coordination vs. Actor Identity

The presence of coordination does not immediately reveal the identity of the coordinating entity. Actor identity represents the specific technical or physical entity operating the network. Threat actors frequently backstop their operations using stolen credentials, purchased engagement services, or hijacked commercial infrastructure5. A coordinated swarm might share French-language political memes, but the actor identity operating the underlying infrastructure could be a digital marketing firm in Egypt or a decentralized hacktivist collective in Eastern Europe15. Analyzing temporal burstiness or network structure reveals the coordination, but establishing actor identity requires deep technical metadata, such as TLS fingerprinting, IP resolution, and digital exhaust tracking.

Actor Identity vs. Sponsorship

In the modern FIMI landscape, the actor operating the keyboard is rarely the ultimate sponsor of the operation. The rise of disinformation-for-hire services allows nation-states and corporate entities to outsource influence operations to private PR firms, creating a layer of plausible deniability17. The actor identity might be a commercial digital marketing agency operating out of Shenzhen or a mercenary proxy group, but the sponsorship—the entity funding and directing the operation—could be a foreign intelligence service aiming to destabilize a geopolitical adversary18. Disentangling the proxy actor from the state sponsor requires analyzing financial flows, client acquisitions, and strategic alignment, moving beyond pure cyber threat intelligence into geopolitical and economic analysis.

Sponsorship vs. Intent or Effect

Finally, identifying the sponsor does not automatically define the intent or guarantee the effect of the campaign. A state sponsor may fund an operation with the intent of polarizing a foreign electorate, but the actual effect might be negligible, resulting in low engagement and rapid platform interdiction2. Alternatively, the intent might not be to convince human readers at all, but rather to exploit "data voids" and poison the training datasets of commercial AI chatbots—a tactic referred to as "LLM grooming"21. Intent is the strategic goal, whereas effect is the measurable impact on the information ecosystem. Both must be evaluated independently of the sponsor's identity to accurately assess the overall systemic risk1.

High-Level Defensive Evidence and Attribution Mechanics

Distinguishing a coordinated AI-assisted network from an organic digital community relies on triangulating multiple streams of behavioral, technical, and linguistic metadata. Modern attribution frameworks, such as the Information Manipulation Sets (IMS) and the Online Operations Kill Chain, emphasize the operational lifecycle of a campaign, dividing investigations into distinct categories of defensive evidence2.

Account Chronology and Temporal Coordination

Synthetic swarms exhibit distinct chronological anomalies that deviate from human behavior. Temporal coordination is mathematically measurable through metrics such as Shannon entropy and temporal burstiness. In a naturally occurring viral event, user engagement follows a recognizable diffusion curve with high entropy, meaning the intervals between shares, the time zones of the actors, and the daily activity rhythms are heterogeneous and varied1. In contrast, coordinated networks exhibit synchronized temporal bursts. When an LLM-driven botnet is directed to push a narrative, the temporal burstiness—represented as [Figure omitted from source export] in algorithmic modeling—spikes unnaturally as hundreds of nodes publish content within milliseconds of each other, or strictly adhere to the working hours of a specific, foreign time zone12. Furthermore, researchers utilize synchronization loss algorithms to detect inter-person temporal coordination, identifying networks that artificially interact with one another to game recommendation algorithms1. The precise chronological alignment of account creation dates also serves as a strong indicator; FIMI networks frequently deploy "sleeper" accounts registered in automated batches on the exact same day, remaining dormant until activated for a specific campaign4.

Network Structure and Cross-Platform Roles

Organic networks are highly clustered but decentralized, forming complex webs of mutual interaction. Coordinated swarms frequently rely on strict, artificial topologies, such as star or tree structures, where central "amplifier" nodes are unconditionally supported by peripheral "sybil" accounts4. These operations are rarely confined to a single platform. Investigators track cross-platform roles, observing how an operation might utilize one platform for secure command-and-control (C2), another for video hosting, a third for algorithmic amplification, and a fourth for backstopping persona credibility14. The mapping of these cross-platform roles reveals a division of labor that is highly indicative of professionalized influence operations.

Reused Infrastructure, Domains, and Hosting

Technical infrastructure provides some of the most concrete evidence of actor identity and sponsorship. Operations frequently recycle infrastructure to minimize costs and deployment times. Investigators track DNS activity, IP overlap, and the use of "bulletproof hosting" services—infrastructure providers located in permissive jurisdictions that ignore international takedown requests and facilitate activities ranging from ransomware to astroturfing16. Advanced fingerprinting techniques, such as JA3 and JA4 TLS fingerprinting, are critical for uncovering CIB infrastructure. When a botnet, automated scraper, or C2 script communicates with a server, the specific structure of its cryptographic handshake (combining the TLS version, accepted ciphers, extensions, and elliptic curves) creates a unique hash6. Threat intelligence analysts use these hashes to detect low-and-slow probing, domain fronting, and automated content generation28. If an entire network of purportedly independent local news websites across thirty countries connects to web servers utilizing the exact same rare JA3 fingerprint and ASN block associated with a known cyber-mercenary, the illusion of independence is mathematically dismantled28. Threat actors also engage heavily in typosquatting and domain spoofing, registering hundreds of URLs that mimic legitimate media organizations to deceive users and search engine crawlers2.

Media Provenance and Profile-Image Origins

The provenance of digital media is a primary battleground in synthetic persona detection. Early botnets relied on stolen profile pictures, detectable via simple reverse image searches14. Modern synthetic personas utilize generative adversarial networks (GANs), such as StyleGAN2, to create unique, non-existent human faces. Investigators detect these by analyzing subtle generative artifacting. Common flaws include asymmetrical backgrounds, structural anomalies in eyewear or jewelry, mismatched earrings, and persistent pupil alignment. GAN-generated faces often feature pupils located at the exact same pixel coordinates across an entire network of profiles, a statistical impossibility in organic photography14.

Linguistic Evidence and Stylometry

Stylometry—the quantitative analysis of linguistic style—serves as a robust forensic tool for identifying AI-generated text and attributing coordinated campaigns31. While LLMs can mimic various tones, they exhibit measurable statistical biases derived from their probability-driven generation. AI-generated text routinely demonstrates low lexical diversity (measured by the Type-Token Ratio, or TTR), over-reliance on transitional phrases, and profound structural predictability31. By calculating semantic homogenization [Figure omitted from source export] and lexical diversity [Figure omitted from source export], analysts can map the "linguistic fingerprint" of an operation. Statistical modeling confirms that synthetic texts possess significantly lower syntactical variance compared to authentic human writing, enabling researchers to classify authorship beyond superficial bag-of-words models31. Furthermore, when translating propaganda across dozens of languages simultaneously, automation leaves distinct traces; machine translations of specific regional dialects often fail to capture socio-cultural idioms, revealing the centralized, non-native origin of the text and indicating industrial-scale translation pipelines21.

Behavioral Patterns and Platform Enforcement Records

Behavioral analysis focuses on the specific tactics, techniques, and procedures (TTPs) utilized across the kill chain of an information operation. These behaviors include deploying evasive techniques like cloaking (serving different content to moderators versus users), weaponizing third-party commercial platforms, and orchestrating targeted harassment14. Historical enforcement data provides a critical baseline for evaluating these patterns. Threat actors frequently rebuild networks using modified tactics after a platform takedown. By cross-referencing current behavioral patterns with historical platform enforcement records, investigators can identify evolutionary linkages—such as a network pivoting from brute-force hashtag amplification to subtle comment-section hijacking—confirming the persistence and adaptability of a specific Advanced Persistent Manipulator (APM)1.

Taxonomic Case Studies in Disinformation Swarms

The theoretical frameworks of FIMI attribution are best understood through the forensic analysis of active operations. The following case studies illustrate the diverse methodologies employed by state-sponsored actors, intelligence services, and commercial proxy firms, demonstrating the spectrum of content generation, coordination, and strategic intent.

Russian Operations and Proxies

1. Doppelganger (Storm-1099): Uncovered in 2022, this campaign utilized domain spoofing and typosquatting (e.g., registering washingtonpost.pm to mimic washingtonpost.com) to clone legitimate Western media outlets. It then deployed networks of synthetic personas, including a notorious cluster known as the "German Odettes," to amplify pro-Russian narratives in social media comment sections, explicitly targeting European support for Ukraine and exploiting user trust in legacy media brands2.

2. Pravda Network (Portal Kombat): Tracked extensively by the French agency Viginum, this network comprises hundreds of centralized domains (e.g., news-pravda.com) producing millions of machine-translated articles annually. The network serves as an aggregator, stripping legitimate content and weaving in Kremlin-aligned disinformation21. Its primary innovation appears to be "LLM grooming"—flooding the internet with SEO-optimized disinformation to deliberately poison the training data and retrieval-augmented generation (RAG) processes of major AI chatbots21.

3. Operation Overload (Matryoshka / Storm-1679): A campaign designed to exhaust the resources of open-source investigators and fact-checkers. Synthetic personas impersonated credible sources and directly contacted researchers with manipulated videos and forged documents, utilizing distraction as a primary weapon while simultaneously targeting Moldovan and European institutions during critical election cycles20.

4. Storm-1516: Known for orchestrating deepfake and AI-generated video campaigns, this actor deployed a network of purportedly AI-generated YouTube channels with paid voice actors posing as local commentators to inject disinformation into North American political ecosystems, including amplifying localized fringe narratives like Alberta secession30.

5. Secondary Infektion: A highly sophisticated Russian operation that prioritized operational security over algorithmic scale. It utilized single-use burner accounts to post forged documents and divisive content on obscure forums, relying on the organic outrage of authentic users to launder the narratives into mainstream discourse. The strict adherence to single-use infrastructure made attribution extremely difficult14.

6. Internet Research Agency (IRA): The classic troll farm model that utilized human operators alongside rudimentary botnets to polarize the 2016 US electorate. The IRA established the blueprint for cross-platform persona backstopping, identity theft, and the exploitation of algorithmic amplification, setting the baseline for modern CIB analysis1.

7. NoName057(16): A pro-Russian hacktivist and FIMI group utilizing bulletproof hosting networks (such as the Dutch-based Mirhosting) to launch coordinated DDoS attacks and synchronized disinformation campaigns against European critical infrastructure, seamlessly blending disruptive cyber warfare with narrative manipulation16.

Chinese Operations and PR Proxies

8. Paperwall: Attributed to the Shenzhen-based PR firm Haimaiyunxiang Media (Haimai) by Citizen Lab, this operation operated at least 123 fake news sites globally. It buried pro-Beijing propaganda and ad hominem attacks against dissidents within an avalanche of scraped, legitimate local news and commercial press releases. By mimicking local newspapers across 30 countries, it exploited the veneer of localized journalism to bypass traditional defenses17.

9. HaiEnergy: Similar in structure to Paperwall, this campaign (linked to the PR firm Haixun) exploited commercial newswire services like Times Newswire. By purchasing distribution, the network forced pro-PRC narratives—including conspiracy theories regarding biological weapons—onto the subdomains of legitimate Western news organizations, leveraging financial transactions for narrative laundering17.

10. Spamouflage (Dragonbridge): A massive, persistent CIB network that relies on sheer volume. It utilizes thousands of newly created or hijacked social media accounts to post pro-China and anti-Western content. While often mocked by researchers for its low engagement and poor operational security, its persistence demonstrates a strategy of continuous information environment saturation rather than targeted persuasion44.

Iranian and Unaligned Operations

11. International Union of Virtual Media (IUVM): An Iranian state-aligned operation that functioned as a massive content repository, laundering regime-friendly narratives, cartoons, and articles through a network of inauthentic websites designed to look like independent NGOs and media collectives. Following the exposure and loss of its assets, the operation rapidly reconstituted new infrastructure14.

12. Storm-2035: A network engaging in multifaceted cyber-enabled influence operations, leveraging synthetic personas to polarize discourse around Middle Eastern geopolitics and target Western audiences with highly tailored, algorithmically optimized outrage content15.

State Intelligence, Military, and Commercial Mercenaries

13. Ghostwriter (Belarus/Russia): This campaign combined traditional cyber espionage with information operations. Operators compromised the content management systems (CMS) of legitimate Eastern European news sites to directly inject fabricated articles regarding NATO, subsequently amplifying them via spoofed emails and synthetic social media personas to manufacture immediate crises14.

14. Bee Interactive (Egypt): Highlighting the deep commercialization of FIMI, this Egyptian marketing firm was implicated in running coordinated inauthentic networks across multiple platforms. It serves as a prime example of how state actors outsource political influence campaigns to commercial entities motivated purely by profit15.

15. South Korean NIS 2012 Campaign: An early, extensively documented instance of state-directed astroturfing. The South Korean National Intelligence Service deployed psychological operations agents to run coordinated Twitter accounts during the 2012 presidential election, leaving distinct traces of coordination that allowed subsequent researchers to establish mathematical ground truth for CIB behavioral metrics5.

16. French Military in the Central African Republic (CAR): In 2020, researchers uncovered a remarkable clash where a network of synthetic personas operated by individuals linked to the French military engaged in direct, platform-level narrative combat with a rival network of Russian synthetic personas over influence in the CAR, demonstrating that democratic states also utilize deceptive CIB tactics in asymmetric information environments15.

The Strategic Exploitation of AI: LLM Grooming vs. Data Voids

The intent behind large-scale, automated content generation—such as the millions of articles produced by the Pravda network—has sparked intense debate regarding the vulnerability of artificial intelligence ecosystems. Security researchers posit that threat actors are engaging in "LLM grooming" or data poisoning. This theory suggests that by flooding the open web with SEO-optimized, coordinated falsehoods, actors aim to gradually convince perpetually updating models that these falsehoods are factual and widely corroborated21. Audits of major AI chatbots indicated that they repeated Kremlin narratives when prompted, citing Pravda sources as legitimate references, suggesting the successful manipulation of AI training data30. However, opposing analytical frameworks suggest these references reflect "data voids" rather than successful manipulation. A data void occurs when a user searches for a highly specific, niche topic where reputable, authoritative information is scarce. In the absence of high-quality mainstream coverage, the LLM defaults to the only available data, which happens to be the high-volume, low-quality disinformation seeded by networks like Pravda13. Under this framework, FIMI operations are not fundamentally altering the base weights of the foundational models; rather, they are exploiting informational scarcity. Regardless of whether the mechanism is active grooming or passive void exploitation, the effect remains the contamination of epistemic integrity via synthetic text30.

The False Positive Paradox: The Danger of Algorithmic Over-Enforcement

The methodologies used to detect FIMI—graph analysis, behavioral heuristics, burstiness algorithms, and stylometric AI-detection—are inherently probabilistic. When platforms and institutions implement these detection frameworks aggressively, the resulting false positives inflict severe collateral damage on legitimate, vulnerable internet users. Distinguishing malicious actors from ordinary automation, pseudonymous humans, and synchronized civic activity is the most critical ethical and operational challenge in modern threat intelligence4.

Ordinary Automation vs. Emergency-Response Communities

Not all automated bot activity is malicious. The failure to establish clear taxonomies between hostile CIB and beneficial civic automation resulted in significant public safety disruptions during the 2023 Twitter (X) API transitions. When the platform imposed strict rate limits and exorbitant pricing models in a blunt effort to eradicate spam bots and unverified automation, it inadvertently dismantled critical emergency-response infrastructure9. Automated accounts operated by the National Weather Service (NWS), which utilized the API to issue split-second, life-saving warnings for tornadoes and tsunamis, were abruptly suspended or rate-limited9. Without API access, forecasters were forced to draft alerts manually, a delay where seconds dictate the difference between life and death9. Similarly, vital civic automation platforms, including the Metropolitan Transportation Authority (MTA) and Bay Area Rapid Transit (BART), were taken offline, depriving millions of citizens of critical transit safety updates48. This incident underscores the severe danger of utilizing broad behavioral heuristics—such as high posting volume or automated chronological triggers—without contextual whitelisting for civic safety networks49.

Pseudonymous Humans, Activists, and Whistleblowers

Threat hunters rely on technical infrastructure analysis to detect evasion tactics, routinely flagging the use of Virtual Private Networks (VPNs), The Onion Router (Tor), and disposable communication channels as indicators of malicious intent14. However, anonymous whistleblowers, marginalized groups, and political dissidents operating in authoritarian regimes rely on the exact same operational security (OPSEC) tools to protect their physical safety. They operate pseudonymous accounts, obscure their IP addresses, and frequently cycle through infrastructure to avoid state surveillance14. If platforms enforce rigid identity verification or penalize accounts exhibiting "evasive infrastructure" signatures, they risk silencing the populations that require digital protection the most, disastrously conflating the operational security of a human rights activist with the evasion tactics of an Advanced Persistent Manipulator.

Synchronized Civic Activity vs. Astroturfing

During periods of political upheaval, organic communities engage in synchronized civic activity. Activists, dissidents, and localized subcultures frequently coordinate to hijack hashtags, mass-report hostile accounts, and amplify political messaging52. To a purely statistical network analyzer, this behavior—characterized by high temporal burstiness, coordinated account creation, and identical messaging—closely mirrors state-sponsored astroturfing. Without qualitative analysis of the actors' identities and the organic nature of their real-world coordination, defensive algorithms risk censoring legitimate democratic mobilization under the guise of combating FIMI.

Role-Players and Fandom Communities

A largely overlooked casualty of algorithmic CIB detection is the role-playing and digital fandom community. Participants in these communities routinely operate multiple pseudonymous "alt-accounts" representing fictional characters. They coordinate their posting schedules based on out-of-character (OOC) planning, utilize shared lore, heavily repost media from within their in-group, and employ highly stylized, repetitive linguistic structures. To algorithmic detectors, such as the widely criticized Botometer, this behavior triggers numerous red flags associated with Sybil networks and bot swarms52. Studies evaluating such automated scoring systems frequently reveal that accounts flagged as "bots" are often highly active, hyper-focused human users participating in niche digital subcultures52.

AI-Content Detectors, Error, and Bias

The deployment of automated AI-text detectors (such as GPTZero, Turnitin, and Originality.ai) in academic and professional settings has exposed profound, systemic biases against specific populations. These detectors do not definitively identify AI; rather, they evaluate text based on two statistical heuristics: perplexity and burstiness32. Perplexity measures how predictable a word choice is to a language model; lower perplexity implies high predictability. Burstiness measures the variance in sentence length and syntactic structure; low burstiness indicates a rigid, uniform rhythm32. LLMs are mathematically optimized to produce text with low perplexity and low burstiness. However, these exact statistical properties are naturally present in the authentic writing styles of two highly vulnerable human populations: neurodivergent individuals and non-native speakers8. The Neurodivergent Penalty: Autistic writers and individuals with severe ADHD frequently exhibit writing styles characterized by hyper-structured formatting, extreme directness, high formality, and an absence of flowery, unpredictable colloquialisms54. Because their writing is highly logical, linear, and devoid of chaotic syntactical variation, their natural prose exhibits the exact "low burstiness" and "low perplexity" that AI detectors are calibrated to flag54. Empirical research and widespread anecdotal evidence demonstrate that neurodivergent students are consistently and falsely accused of academic misconduct because their neurological communication style perfectly mirrors the statistical predictability of an LLM8. The reliance on AI detectors essentially penalizes neurodivergent individuals for failing to write with neurotypical linguistic variance, subjecting them to severe academic and professional disciplinary actions55. Non-Native Writers and Minority-Language Communities: Similarly, researchers have demonstrated that non-native writers (ESL students) suffer from extreme false-positive rates when subjected to AI detection algorithms. In structured tests, AI detectors falsely flagged over 60% of TOEFL essays written by non-native speakers as AI-generated7. Non-native speakers naturally rely on simpler grammatical structures, standard vocabulary, and uniform sentence lengths as they navigate a secondary language. This structural uniformity triggers low perplexity scores32. Furthermore, minority-language communities suffer due to the lack of linguistic diversity in the foundational training data of these detectors. When evaluating low-resource languages (such as Scottish Gaelic or Maori, which the Pravda network actively targeted using simplistic, biblical-corpus-driven machine translation21), algorithmic detectors fail to distinguish between authentic, culturally nuanced writing and synthetic slop. Deploying these tools without acknowledging their mathematical limitations results in a digital redlining effect, where algorithms classify the diverse spectrum of human communication through a flattened, discriminatory standard of what constitutes "authentic" human writing7.

Conclusion

The detection of synthetic personas and disinformation swarms has transcended the identification of rudimentary spam scripts. It is a highly complex, adversarial discipline requiring the synthesis of cryptographic forensics, stylometry, and behavioral science. As demonstrated by the operations of the Pravda Network, Paperwall, and sophisticated state-sponsored APMs, threat actors are continuously evolving, leveraging AI to achieve unprecedented scale and exploiting commercial PR infrastructure to obscure their attribution and intent. However, the defensive mechanisms engineered to counter these threats remain blunt statistical instruments. The mathematical models that identify coordinated burstiness, shared infrastructure, and low linguistic perplexity cannot intrinsically distinguish between a hostile intelligence service, a critical weather-warning API, an anonymous dissident utilizing a VPN, a role-playing subculture, or a neurodivergent student submitting an essay. A mature, risk-based approach to FIMI attribution must therefore prioritize deep contextual analysis, ensuring that the necessary crusade to secure the digital ecosystem does not inadvertently weaponize algorithmic bias against the most vulnerable participants within it.

Works cited

1. Theoretical Bases of Methods of Counteraction to Modern Forms of Information Warfare, https://www.mdpi.com/2073-431X/14/10/410

2. Doppelganger hub \- EU DisinfoLab, https://www.disinfo.eu/doppelganger-hub/

3. NETWORKS OF INAUTHENTIC NEWS SITES AND THE RISK OF HOSTILE INFORMATION CAMPAIGNS IN SINGAPORE \- S. Rajaratnam School of International Studies (RSIS), https://www.rsis.edu.sg/wp-content/uploads/2024/10/PR241025\_Networks-of-Inauthentic-News-Sites-the-Risk-of-Hostile-Information-Campaigns-in-SG.pdf

4. The Physics of News, Rumors, and Opinions \- arXiv, https://arxiv.org/html/2510.15053v1

5. Online astroturfing: A problem beyond disinformation \- ResearchGate, https://www.researchgate.net/publication/361389437\_Online\_astroturfing\_A\_problem\_beyond\_disinformation

6. The Art of Cyber-War, Invincibility Lies in Defense \- Darktrace, https://www.darktrace.com/blog/the-art-of-cyber-war

7. Writing Under Surveillance: The Problem with AI Detection \- Visible Pedagogy, https://vp.commons.gc.cuny.edu/2026/05/18/writing-under-surveillance-ai-detection/

8. The misclassification of autistic writing as AI-generated \- arXiv, https://arxiv.org/html/2607.14729v1

9. National Weather Service accounts were not granted API exemptions by Twitter \- Mashable, https://mashable.com/article/twitter-exemptions-nws-public-service-safety-weather-accounts

10. Who Owns This Agent? Tracing AI Agents Back to Their Owners \- arXiv, https://arxiv.org/pdf/2605.16035

11. AI Slop and the Information Ecosystem \- Institute of Global Politics, https://igp.sipa.columbia.edu/sites/igp/files/2026-06/AI%20Slop%20and%20the%20Information%20Ecosystem\_IGP%20Report.pdf

12. Detecting Synthetic Political Narratives in Cross-Platform Social Media Discourse \- arXiv, https://arxiv.org/html/2605.21540v1

13. LLMs grooming or data voids? LLM-powered chatbot references to Kremlin disinformation reflect information gaps, not manipulation \- HKS Misinformation Review, https://misinforeview.hks.harvard.edu/wp-content/uploads/2025/10/alyukov\_chatbots\_kremlin\_disinformation\_20251015.pdf

14. Phase-based Tactical Analysis of Online Operations, https://carnegieendowment.org/research/2023/03/phase-based-tactical-analysis-of-online-operations

15. List of political disinformation website campaigns \- Wikipedia, https://en.wikipedia.org/wiki/List\_of\_political\_disinformation\_website\_campaigns

16. Sanctioned, Seized, Still Scanning: Inside a Russian Bulletproof Hosting Network Targeting the EU | ELLIO Blog, https://ellio.tech/en/blog/sanctioned-seized-still-scanning-inside-a-russian-bulletproof-hosting-network-targeting-the-eu/

17. PAPERWALL: Chinese Websites Posing as Local News Outlets Target Global Audiences with Pro-Beijing Content \- The Citizen Lab, https://citizenlab.ca/research/paperwall-chinese-websites-posing-as-local-news-outlets-with-pro-beijing-content/

18. Uncovering Paperwall, the “glocal” network of Chinese propaganda \- Decode39, https://decode39.com/8726/paperwall-china-local-websites-influence-operation/

19. 234 Findings • The People's Republic of China (PRC) continued to rely on a National Anti-Fraud Center (NAFC) app to collect, https://www.cecc.gov/sites/evo-subsites/cecc.house.gov/files/2024-12/2024-CH16-Technology-Enhanced-Authoritarianism.pdf

20. Operation Overload's underwhelming influence and evolving tactics, https://www.isdglobal.org/digital-dispatch/operation-overloads-underwhelming-influence-and-evolving-tactics/

21. Pravda network \- Wikipedia, https://en.wikipedia.org/wiki/Pravda\_network

22. LLMs grooming or data voids? LLM-powered chatbot references to Kremlin disinformation reflect information gaps, not manipulation | HKS Misinformation Review, https://misinforeview.hks.harvard.edu/article/llms-grooming-or-data-voids-llm-powered-chatbot-references-to-kremlin-disinformation-reflect-information-gaps-not-manipulation/

23. “PSYOPS”, State-sponsored online disinformation in international law \- Kobe University, https://da.lib.kobe-u.ac.jp/da/kernel/0100482421/D1008673.pdf

24. SAN2020 \- Sociedad Argentina de Investigación en Neurociencias, https://saneurociencias.org.ar/wp-content/uploads/2023/07/EBOOK-2020.pdf

25. Track: Poster Session 1 Pavilion 4 \- ICLR 2026, https://iclr.cc/virtual/2026/session/10021369

26. America Resilient in the Face of Aggressive Foreign Malign Influence Targeting the 2024 U.S. Elections \- FDD, https://www.fdd.org/analysis/2024/12/18/america-resilient-in-the-face-of-aggressive-foreign-malign-influence-targeting-the-2024-u-s-elections/

27. IGF 2026 Proposed Issues | Internet Governance Forum (IGF), https://www.intgovforum.org/en/igf-2026-proposed-issues

28. Threat intelligence and situational awareness for better security \- NOTIONES, https://www.notiones.eu/2023/09/25/threat-intelligence-and-situational-awareness-for-better-security/

29. You Are Not Too Small for Nation-State Attackers \- CyberOne, https://cyberone.security/blog/you-are-not-too-small-for-nation-state-attackers

30. Entity: Pravda disinformation network \- AI Incident Database, https://incidentdatabase.ai/entities/pravda-disinformation-network/

31. Stylometric Approaches for AI-Text Identification Author: Emmanuel Joseph, Arthur Graham, Matilda Ashford Date: 19/10/2025 \- ResearchGate, https://www.researchgate.net/profile/Emmanuel-Joseph-24/publication/398588165\_Stylometric\_Approaches\_for\_AI-Text\_Identification/links/693b2bfb9aa6b4649dbfb20f/Stylometric-Approaches-for-AI-Text-Identification.pdf

32. How to Detect AI Patterns in Writing? \- Search Atlas, https://searchatlas.com/blog/ai-patterns-in-writing/

33. Is this hotel review truthful or deceptive? A platform for disinformation detection through computational stylometry \- ACL Anthology, https://aclanthology.org/2020.stoc-1.6.pdf

34. Fingerprinting Fine-tuned Language Models in the Wild \- Computer Science | UC Davis Engineering, https://web.cs.ucdavis.edu/\~zubair/files/fingerprint-lm-acl2021.pdf

35. A Framework for Attribution of Information Influence Operations \- Psychological Defence Research Institute, https://www.psychologicaldefence.lu.se/sites/psychologicaldefence.lu.se/files/2025-02/250131\_ADACio%20D1.1\_Attribution%20Framework%20Report\_Final.pdf

36. Global offensive: \- Mapping the Sources Behind the Pravda Network \- GLOBSEC, https://www.globsec.org/sites/default/files/2025-05/Pravda%20Network%20Report.pdf

37. SGDSN \- Portal Kombat, https://www.sgdsn.gouv.fr/files/files/20240212\_NP\_SGDSN\_VIGINUM\_PORTAL-KOMBAT-NETWORK\_ENG\_VF.pdf

38. Taming the Machines: How Russian Propaganda is Training AI Language Models, https://ridl.io/taming-the-machines-how-russian-propaganda-is-training-ai-language-models/

39. Democratic defense against disinformation 2.0 \- Atlantic Council, https://www.atlanticcouncil.org/in-depth-research-reports/report/democratic-defense-against-disinformation-2-0/

40. The Growth of Chinese Fake News Sites and the Spread of Disinformation— Articles from South Korean Media Used to Fuel Divisions in Japanese Society, https://www.spf.org/iina/en/articles/ryohei\_suzuki\_01.html

41. Baybridge. Anatomy of a chinese information influence ecosystem | Irsem, https://www.irsem.fr/publications/baybridge-anatomy-of-a-chinese-information-influence-ecosystem

42. Google takes down fake news sites, wire services run by Chinese influence operation, https://therecord.media/google-fake-news-china-outlets

43. National Cyber Security Center has detected influence operations exploiting China's “disguised media websites.” | by S2W \- Medium, https://medium.com/s2wblog/national-cyber-security-center-has-detected-influence-operations-exploiting-chinas-disguised-d7eb46d0d8a5

44. Disinformation diplomacy: How malign actors are seeking to undermine democracy, https://publications.parliament.uk/pa/cm5901/cmselect/cmfaff/703/report.html

45. Is Russia grooming AI chatbots? \- UTS, https://www.uts.edu.au/news/2025/11/is-russia-grooming-ai-chatbots

46. LLMs grooming or data voids? LLM-powered chatbot references to Kremlin disinformation reflect information gaps, not manipulation \- Research Explorer The University of Manchester, https://research.manchester.ac.uk/en/publications/llms-grooming-or-data-voids-llm-powered-chatbot-references-to-kre/

47. Countering Disinformation Effectively: An Evidence-Based Policy Guide, https://carnegieendowment.org/research/2024/01/countering-disinformation-effectively-an-evidence-based-policy-guide

48. Elon Musk's Twitter Tweaks Nuked These 13 Useful Emergency Service Bots \- Gizmodo, https://gizmodo.com/twitter-elon-musk-mta-nws-bart-noaa-bots-api-shut-down-1850345806

49. NWS: Don't Only Rely on Twitter for Weather Information \- Campus Safety Magazine, https://www.campussafetymagazine.com/news/nws-dont-only-rely-on-twitter-for-weather-information/122755/

50. Twitter emergency alerts U-turn announced; Musk threatens NPR \- 9to5Mac, https://9to5mac.com/2023/05/03/twitter-emergency-alerts/

51. WORK BY THE FORUM AGAINST DISINFORMATION CAMPAIGNS 2024 INITIATIVES \- Departamento de Seguridad Nacional, https://www.dsn.gob.es/sites/default/files/2025-07/Disinformation%20Campaigns%202024%20Initiatives.pdf

52. \[2207.11474\] Investigating the Validity of Botometer-based Social Bot Studies \- arXiv, https://arxiv.org/abs/2207.11474

53. Botometer \- Propaganda Critic, https://propagandacritic.com/index.php/tools-for-fighting-back/botometer/

54. Can AI Detectors Mistake Neurodivergent Writing for AI-Generated Text? \- DecEptioner Blog, https://deceptioner.site/blog/can-ai-detectors-flag-neurodivergent-writing-styles

55. Flagged by AI for “sounding like AI”—neurodivergent writing styles shouldn't be penalized. : r/neurodiversity \- Reddit, https://www.reddit.com/r/neurodiversity/comments/1jx2q3q/flagged\_by\_ai\_for\_sounding\_like\_aineurodivergent/

56. AI Detectors Flag Autistic and ADHD Writers — The Evidence \- GradPilot, https://gradpilot.com/news/ai-detectors-neurodivergent-students

57. The Misclassification of Autistic Writing as AI-Generated \- ResearchGate, https://www.researchgate.net/publication/394678751\_The\_Misclassification\_of\_Autistic\_Writing\_as\_AI-Generated

58. AI Concerns for the Neurodivergent \- LLF National Law Firm, https://www.studentdisciplinedefense.com/ai-concerns-for-the-neurodivergent