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arxiv: 2606.00136 · v1 · pith:HKFJREGGnew · submitted 2026-05-28 · 💻 cs.LG · cs.AI· cs.CL· cs.CR· cs.SI

Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

Jonghyun Chung , Rishabh Chaddha , Sanket Badhe , Debanshu Das , Nathan Huang , Amanpreet Kaur This is my paper

Pith reviewed 2026-06-29 08:31 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CLcs.CRcs.SI
keywords generative AIinauthentic narrativesproactive detectionC5 Interaction Modellifecycle taxonomycoordinated inauthentic behaviordigital ecosystem resiliencedistributional drift
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The pith

The C5 Interaction Model enables a unified lifecycle taxonomy for proactive detection of GenAI-generated inauthentic narratives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This survey synthesizes research showing that reactive detection methods fail against the speed and scale of Generative AI synthetic content. It proposes shifting to proactive detection by organizing analysis around a single lifecycle taxonomy. The C5 Interaction Model structures adversarial campaigns into five stages to combine social science models of narrative spread with machine learning techniques for early identification. This integration covers anomaly detection in embeddings, graph-based coordination analysis, and agentic systems while handling issues like distributional drift. A reader would care because it offers a practical way to build systems that intervene before synthetic narratives amplify.

Core claim

The paper claims that structuring analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences) integrates socio-technical lifecycle models of adversarial campaigns with computational methods, allowing proactive detection of emerging inauthentic narratives at multiple stages through techniques including Hawkes processes, anomaly detection in high-dimensional spaces, unsupervised coordination detection on multi-layer graphs, and agentic AI systems, while outlining a research agenda to address GenAI-driven challenges like rapid threat evolution and multi-level distributional drift.

What carries the argument

The C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), which provides the organizing taxonomy that combines social science lifecycle views of narrative campaigns with machine learning detection methods across creation, seeding, and propagation stages.

If this is right

  • Analysis of Coordinated Inauthentic Behavior, epidemiological modeling, and Hawkes processes can separate synthetic amplification patterns from authentic baseline traffic.
  • Proactive techniques such as anomaly detection in embedding spaces, graph-based coordination detection, and agentic AI systems can intervene at specific points in the C5 stages.
  • Future systems must account for multi-level distributional drift to maintain detection performance as GenAI threats evolve.
  • Building anticipatory and resilient information ecosystems requires focusing on anomalous cluster detection rather than post-spread identification.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The taxonomy could be tested by mapping historical non-GenAI misinformation campaigns onto the same five stages to check consistency.
  • Adding quantitative measures of how completely the C5 stages cover observed threat data would help evaluate the model's claimed integration value.
  • Extending the model to incorporate platform-specific incentives in the Causes stage might reveal new intervention points not addressed in the survey.

Load-bearing premise

The C5 Interaction Model supplies a comprehensive and non-redundant structure that can integrate machine learning and social science research streams on adversarial campaigns without leaving significant gaps or creating overlaps.

What would settle it

A test applying the C5 categories to a fresh set of documented GenAI campaigns and finding either a major threat type that fits none of the five stages or two stages that capture the same phenomenon in practice would show the model fails as a unifying taxonomy.

Figures

Figures reproduced from arXiv: 2606.00136 by Amanpreet Kaur, Debanshu Das, Jonghyun Chung, Nathan Huang, Rishabh Chaddha, Sanket Badhe.

Figure 1
Figure 1. Figure 1: FIGURE 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIGURE 2 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIGURE 3 [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
read the original abstract

The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives. In this survey, we adopt a unified, lifecycle-based taxonomy to combine socio-technical lifecycle models of adversarial campaigns with advanced computational methodologies for emerging inauthentic narrative detection. By structuring the analysis around the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences), we integrate different research streams from machine learning and social science. To differentiate spread patterns of synthetic amplification from authentic baseline traffic, this paper surveys state-of-the-art techniques for modeling the creation, seeding, and propagation of fresh narratives, including the analysis of Coordinated Inauthentic Behavior (CIB), epidemiological modeling, and Hawkes process. This survey also provides a systematic review of proactive detection methods for adversarial threats at different stages in the C5 interaction model, specifically, anomaly detection in high-dimensional embedding spaces, unsupervised coordination detection on multi-layer graphs, and agentic AI systems. Finally, this survey addresses challenges posed by GenAI, including the difficulty of tracking rapidly changing threats and multi-level distributional drift, and it outlines a future research agenda focused on detecting anomalous clusters and building anticipatory and resilient systems. This survey provides a comprehensive, lifecycle-based review of methods for the proactive detection of emerging synthetic threats for more resilient information ecosystems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. This survey synthesizes research on Generative AI-enabled adversarial synthetic content, arguing for a paradigm shift from reactive to proactive detection of emerging inauthentic narratives. It introduces the C5 Interaction Model (Context, Causes, Content, Cycle of Amplification, Consequences) as a unified lifecycle-based taxonomy that integrates machine learning techniques (e.g., Hawkes processes, embedding anomaly detection, multi-layer graph coordination detection) with social science concepts (e.g., Coordinated Inauthentic Behavior analysis) to model narrative creation, seeding, propagation, and consequences. The paper reviews state-of-the-art methods at each C5 stage and outlines challenges such as distributional drift along with a future research agenda for anticipatory resilient systems.

Significance. If the C5 taxonomy is shown to be both comprehensive and non-redundant, the survey could consolidate disparate ML and social-science streams into a practical framework for proactive threat detection, aiding the design of anticipatory systems against GenAI-driven misinformation. The explicit coverage of techniques such as Hawkes processes for amplification modeling and unsupervised coordination detection on multi-layer graphs provides a useful consolidation of current methods. However, without demonstrated validation of the taxonomy, the claimed unification and paradigm shift remain assertions rather than evidenced contributions.

major comments (3)
  1. [section introducing and applying the C5 Interaction Model] The central claim that the C5 Interaction Model supplies a comprehensive, non-redundant structure capable of integrating distinct ML and social-science research streams is asserted through categorization (e.g., placing Hawkes processes and CIB analysis under Cycle of Amplification) but is not supported by any explicit overlap/gap analysis or audit demonstrating that the five categories are disjoint and exhaustive. This validation is load-bearing for the unification and paradigm-shift argument.
  2. [introductory description of the survey approach] No description is provided of the literature search methodology, selection criteria, database sources, or process for resolving conflicts among cited works. Without these, the survey's coverage, balance, and representativeness of the synthesized research streams cannot be assessed, undermining the claim of a systematic review.
  3. [sections on proactive detection methods and challenges] The demonstration of a 'paradigm shift toward the proactive detection' rests on structuring existing techniques under C5 headings, yet the manuscript supplies no comparative analysis or evidence quantifying how reactive methods fail relative to the proactive approaches surveyed (e.g., anomaly detection in high-dimensional embeddings).
minor comments (2)
  1. [abstract and introduction] The initial presentation of the C5 acronym would benefit from an immediate parenthetical expansion of all five components rather than deferring the full definition.
  2. [review of state-of-the-art techniques] Several technique descriptions (e.g., multi-layer graph coordination detection) would be clearer with explicit pointers to the primary references rather than general citations.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and outline revisions to strengthen the manuscript's claims regarding the C5 model, survey methodology, and evidence for the paradigm shift.

read point-by-point responses

  1. Referee: [section introducing and applying the C5 Interaction Model] The central claim that the C5 Interaction Model supplies a comprehensive, non-redundant structure capable of integrating distinct ML and social-science research streams is asserted through categorization (e.g., placing Hawkes processes and CIB analysis under Cycle of Amplification) but is not supported by any explicit overlap/gap analysis or audit demonstrating that the five categories are disjoint and exhaustive. This validation is load-bearing for the unification and paradigm-shift argument.

    Authors: We agree that an explicit overlap/gap analysis would provide stronger support for the C5 model's claimed comprehensiveness and non-redundancy. The current manuscript derives the taxonomy from established socio-technical lifecycle models and maps computational methods onto its stages, but does not include a dedicated audit. In revision, we will add a new subsection following the C5 model introduction that systematically maps representative papers to each category, discusses potential boundary overlaps (e.g., between Content and Cycle of Amplification), and justifies the five categories as disjoint and exhaustive based on distinct phases of narrative emergence and impact. revision: yes

  2. Referee: [introductory description of the survey approach] No description is provided of the literature search methodology, selection criteria, database sources, or process for resolving conflicts among cited works. Without these, the survey's coverage, balance, and representativeness of the synthesized research streams cannot be assessed, undermining the claim of a systematic review.

    Authors: This is a valid observation; the manuscript does not detail the literature review process. We will add a new 'Survey Methodology' subsection in the introduction that specifies the databases (arXiv, Google Scholar, ACM Digital Library), search keywords and time range, inclusion/exclusion criteria focused on GenAI-enabled adversarial content, and the process for resolving citation conflicts via author discussion and cross-validation against recent surveys. revision: yes

  3. Referee: [sections on proactive detection methods and challenges] The demonstration of a 'paradigm shift toward the proactive detection' rests on structuring existing techniques under C5 headings, yet the manuscript supplies no comparative analysis or evidence quantifying how reactive methods fail relative to the proactive approaches surveyed (e.g., anomaly detection in high-dimensional embeddings).

    Authors: We acknowledge that the paradigm-shift argument is primarily demonstrated through synthesis and structuring rather than direct quantitative comparison. The introduction and challenges sections note limitations of reactive methods (e.g., inability to handle distributional drift), but do not provide side-by-side metrics. In revision, we will expand the challenges section with a concise comparative discussion, drawing on cited works to illustrate specific failure modes of reactive detection (such as delayed response to novel embeddings) versus the anticipatory capabilities of the surveyed proactive techniques, while noting that a full empirical benchmark lies outside the survey's scope. revision: yes

Circularity Check

0 steps flagged

No circularity: survey organizes external literature without self-referential derivation

full rationale

This is a survey paper whose central contribution is an organizational taxonomy (C5 Interaction Model) used to categorize existing research streams from machine learning and social science. No mathematical derivations, equations, fitted parameters, or predictions appear in the abstract or described structure. The C5 headings simply group cited external methods (Hawkes processes, embedding anomaly detection, CIB analysis, etc.) rather than deriving new results from prior definitions within the paper itself. No self-citation chains, ansatzes, or uniqueness theorems are invoked as load-bearing premises. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding for survey syntheses.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The survey rests on the introduction of the C5 model as the central organizing device and on the assumption that surveyed techniques can be meaningfully grouped under its five stages; no free parameters or external benchmarks are mentioned.

axioms (1)
  • domain assumption The C5 Interaction Model captures the essential stages of adversarial synthetic content campaigns without significant omission or overlap.
    Invoked throughout the abstract to structure the entire review of detection methods and modeling techniques.
invented entities (1)
  • C5 Interaction Model no independent evidence
    purpose: Provide a unified lifecycle taxonomy that integrates socio-technical and computational approaches to inauthentic narrative detection.
    Presented as the core contribution that enables the synthesis of previously separate research streams.

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discussion (0)

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Reference graph

Works this paper leans on

91 extracted references · 7 canonical work pages · 1 internal anchor

  1. [1]

    A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,

    K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Computing Surveys, vol. 53, no. 5, pp. 1–40, Sep. 2021

    2021

  2. [2]

    A Survey on Proactive De- fense Strategies Against Misinformation in Large Language Models,

    S. Liu, H. Liu, A. Liu, B. Duan, Q. Zheng, Y. Yan, H. Geng, P. Jiang, J. Liu, and X. Hu, “A Survey on Proactive De- fense Strategies Against Misinformation in Large Language Models,” in Findings of the Association for Computational Linguistics: ACL 2025, 2025, pp. 18144–18155

    2025

  3. [3]

    Fact- checking information from large language models can de- crease headline discernment,

    M. DeVerna, H. Yan, K. Yang, and F. Menczer, “Fact- checking information from large language models can de- crease headline discernment,” Proc. Nat. Acad. Sci., vol. 121, no. 50, Dec. 2024

    2024

  4. [4]

    AI-Powered Fact-Checking: Combating Misinforma- tion in the Digital Age,

    Q. Li, “AI-Powered Fact-Checking: Combating Misinforma- tion in the Digital Age,” Front. Humanit. Soc. Sci., vol. 5, no. 4, Apr. 2025

    2025

  5. [5]

    Fake News Detection Revisited: An Extensive Review of Theoret- ical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas,

    S. Harris, H. J. Hadi, N. Ahmad, and M. A. Alshara, “Fake News Detection Revisited: An Extensive Review of Theoret- ical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas,” Computers, vol. 12, no. 11, p. 222, 2024

    2024

  6. [6]

    Dis- information as process: modeling the lifecycle of deceit,

    V. Tolz, S. Hutchings, V. Kazakov, and S. Tipaldou, “Dis- information as process: modeling the lifecycle of deceit,” Communication Theory, vol. 35, no. 4, pp. 214, 2025

    2025

  7. [7]

    Misinformation, disinformation, and fake news: Lessons from an interdisciplinary, systematic literature review,

    E. Broda and J. Strömbäck, “Misinformation, disinformation, and fake news: Lessons from an interdisciplinary, systematic literature review,” Ann. Int. Commun. Assoc., vol. 48, no. 2, pp. 139–166, 2024

    2024

  8. [8]

    Thestructureandfunctionofcommunication in society,

    H.D.Lasswell,“Thestructureandfunctionofcommunication in society,” in The Communication of Ideas, L. Bryson, Ed. New York, NY, USA: Harper and Row, 1948, pp. 37–51

    1948

  9. [9]

    The disinformation lifecycle: an integrated understanding of its creation, spread and effects,

    K. Krujiver, N. Finlayson, B. Cadet, and S. Meer , “The disinformation lifecycle: an integrated understanding of its creation, spread and effects,” AI & Soc., 2025

    2025

  10. [10]

    Examining the Role of Distrust in Science and Social Media Use: Effects on Susceptibility to COVID Misperceptions with Panel Data,

    S. Lee, S. Jones-Jang, M. Chung, E. Lee, and T. Diehl, “Examining the Role of Distrust in Science and Social Media Use: Effects on Susceptibility to COVID Misperceptions with Panel Data,” Mass Commun. Soc., vol. 27, no. 2, Oct. 2023

    2023

  11. [11]

    Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intel- ligence,

    S. Gruener, “Determinants of Gullibility to Misinformation: A Study of Climate Change, COVID-19 and Artificial Intel- ligence,” J. Interdisc. Econ., 2022

    2022

  12. [12]

    Epidemiological modeling of news and rumors on Twitter,

    F. Jin, E. Dougherty, P. Saraf, Y. Cao, and N. Ramakrishnan, “Epidemiological modeling of news and rumors on Twitter,” in Proc. 7th Workshop on Social Network Mining and Anal- ysis, 2013

    2013

  13. [13]

    Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theo- ries,

    M. Gurung and N. Agarwal, “Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theo- ries,” in Advances in Social Networks Analysis and Mining, Springer, 2024

    2024

  14. [14]

    Analysing Deception in Witness Memory Though Linguistic Styles,

    S. Sola-Sales, C. Alzetta, C. Moret-Tatay, and F. Dell’Orietta, “Analysing Deception in Witness Memory Though Linguistic Styles,” MDPI Brain Sciences, vol. 13, no. 2, p. 317, 2023

    2023

  15. [15]

    Understanding the Contribution of Recommendation Algorithms on Misinforma- tion Recommendation and Misinformation Dissemination on Social Networks,

    R. Pathak, F. Spezzano, and M. S. Pera, “Understanding the Contribution of Recommendation Algorithms on Misinforma- tion Recommendation and Misinformation Dissemination on Social Networks,” ACM Trans. Web, vol. 17, 2023

    2023

  16. [16]

    The Spread Mechanism of Misinformation on Social Media,

    P. Chen, “The Spread Mechanism of Misinformation on Social Media,” J. Comp. Theor. Educ. Res., Feb. 2025

    2025

  17. [17]

    A Model of Online Misinformation,

    D. Acemoglu, A. Ozdaglar, and J. Siderius, “A Model of Online Misinformation,” NBER, Working Paper 28884, 2021

    2021

  18. [18]

    The Role of Cognitive Bias and Emotional Drive in the Dissemination of Fake News: A Review and Prospectus,

    Y. Luo, “The Role of Cognitive Bias and Emotional Drive in the Dissemination of Fake News: A Review and Prospectus,” Int. J. Fine Arts Creat. Innov., vol. 2, no. 2, Sep. 2025

    2025

  19. [19]

    Cascading falsehoods: mapping the diffusion of misinformation in algorithmic environments,

    D. Shin, and E. Y. Shin, “Cascading falsehoods: mapping the diffusion of misinformation in algorithmic environments,” AI & Soc., 2025, pp. 8770–8780

    2025

  20. [20]

    Countering AI-generated misinformation with pre-emptive source discreditation and debunking,

    E. R. Spearing, C. I. Gile, A. L. Fogwill, T. Prike, B. S. Thompson, and S. Lewandowsky, “Countering AI-generated misinformation with pre-emptive source discreditation and debunking,” R. Soc. Open Sci., vol. 12, no. 6, Jun. 2025

    2025

  21. [21]

    The Power of a Good Idea: Quantitative Modeling of the Spread of Ideas from Epidemiological Mod- els,

    L. M. A. Bettencourt, A. Cintrón-Arias, D. I. Kaiser, and C. Castillo-Chávez“The Power of a Good Idea: Quantitative Modeling of the Spread of Ideas from Epidemiological Mod- els,” Physica A, 2011

    2011

  22. [22]

    A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming,

    P. Zhou, L. Wang, Z. Liu, Y. Hao, P. Hui, S. Tarkoma, and J. Kangasharju, “A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming,” arXiv preprint arXiv:2404.16038, 2024

    work page arXiv 2024

  23. [23]

    Transforming Content Creation: The Influence of Generative AI on a New Frontier,

    V. Divya1 and A. U. Mirza, “Transforming Content Creation: The Influence of Generative AI on a New Frontier,” in Exploring the Frontiers of Artificial Intelligence, 2024

    2024

  24. [24]

    Blessing or curse? A survey on the Impact of Generative AI on Fake News,

    A. Loth, M. Kappes, and M.-O. Pahl, “Blessing or curse? A survey on the Impact of Generative AI on Fake News,” arXiv preprint arXiv:2404.03021, 2024

    work page arXiv 2024

  25. [25]

    Malicious Use of Artificial Intelligence,

    C. Easttom, “Malicious Use of Artificial Intelligence,” in 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), 2025, pp. 00499–00507

    2025

  26. [26]

    Hallu- cinations in Large Language Models (LLMs),

    G. P. Reddy, Y. V. Pavan Kumar, and K. P. Prakash, “Hallu- cinations in Large Language Models (LLMs),” in 2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream), 2024, pp. 1–6

    2024

  27. [27]

    ChildGAN: Face aging and rejuvenation to find missing children,

    P. K. Chandaliya and N. Nain, “ChildGAN: Face aging and rejuvenation to find missing children,” Pattern Recognit., vol. 129, p. 108761, Sep. 2022, doi: 10.1016/j.patcog.2022.108761

    work page doi:10.1016/j.patcog.2022.108761 2022

  28. [28]

    Chinese propaganda network on Facebook used AI-generated faces,

    T. Hatmaker. “Chinese propaganda network on Facebook used AI-generated faces,” TechCrunch. https://techcrunch .com/2020/09/22/facebook-gans-takes-down-networks-o f-fake-accounts-originating-in-china-and-the-philippines (accessed Jan. 9, 2026)

    2020

  29. [29]

    AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones,

    E. J. Miller, B. A. Steward, and A. Dawel, “AI Hyperrealism: Why AI Faces Are Perceived as More Real Than Human Ones,” Psychological Science, vol. 34, no. 12, pp. 1390–1403, 2023

    2023

  30. [30]

    Leveraging generative AI for personalized recommendation system,

    P. Bedi, S. Das, S. B. Goyal, R. Nath Shaw, and A. Ghosh “Leveraging generative AI for personalized recommendation system,” in Advanced Computing and Intelligent Technolo- gies, vol. 958. Singapore: Springer, 2024, pp. 433–450

    2024

  31. [31]

    Integrating Research on AI-Driven Hyper-Personalization: A Review and Framework for Scalable Social Media Campaigns,

    S. Patil, A. Bhat, N. Jain, and V. Javalkar, “Integrating Research on AI-Driven Hyper-Personalization: A Review and Framework for Scalable Social Media Campaigns,” in 2025 International Conference on Pervasive Computational Technologies (ICPCT), 2025, pp. 766–771

    2025

  32. [32]

    Generative AI in Content Creation: Opportunities and Ethical Challenges,

    C. S. Suri, S. S. Shukla, R. Pal, V. Srivastava, G. Talele, and M. K. B, “Generative AI in Content Creation: Opportunities and Ethical Challenges,” in 2024 IEEE 11th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), 2024, pp. 1–6

    2024

  33. [33]

    A Comprehensive Survey on AI Content Generator,

    P. P. Jacob, A. A J, A. Vimal, A. A, and A. K. G, “A Comprehensive Survey on AI Content Generator,” 2023

    2023

  34. [34]

    Analysis of Tools forGenerationofEducationalContentUsingArtificialIntelli- gence,

    I. Blagoev, G. Vassileva, and V. Monov, “Analysis of Tools forGenerationofEducationalContentUsingArtificialIntelli- gence,” in EDULEARN23 Proceedings, 2023, pp. 5078–5086

    2023

  35. [35]

    Advancements and Challenges of Generative AI inHigherEducationalContentCreationATechnicalPerspec- tive,

    A. Faccia, M. Ridon, Z. Beebeejaun, and N. M. Roxana Mosteanu, “Advancements and Challenges of Generative AI inHigherEducationalContentCreationATechnicalPerspec- tive,” in ACM Proc. 2023 8th Int. Conf. Inf. Syst. Eng., 2024, pp. 48–54

    2023

  36. [36]

    How generative AI Is shaping the future of marketing,

    D. Grewal, C. B. Satornino, T. Davenport, and A. Guha, “How generative AI Is shaping the future of marketing,” J. Acad. Mark. Sci., vol. 53, no. 3, pp. 702–722, 2025. 12 VOLUME 1, 2026 Authoret al.: Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey

    2025

  37. [37]

    Using ChatGPT in content marketing: enhancing users’ social media engagement in cross-platform content creation through generative AI,

    K. Aldous, J. Salminen, A. Farooq, S. Jung, and B. Jansen “Using ChatGPT in content marketing: enhancing users’ social media engagement in cross-platform content creation through generative AI,” in Proc. 35th ACM Conf. Hypertext and Social Media, 2024, pp. 376–383

    2024

  38. [38]

    Use of artificial intelligence for the generation of media content,

    B. Sančanin and A. Penjišević, “Use of artificial intelligence for the generation of media content,” Social informatics journal, vol. 1, no. 1, pp. 1–7, 2022

    2022

  39. [39]

    Coordinated inauthentic behavior: An innova- tive manipulation tactic to amplify COVID-19 anti-vaccine communicationoutreachviasocialmedia,

    M. Murero, “Coordinated inauthentic behavior: An innova- tive manipulation tactic to amplify COVID-19 anti-vaccine communicationoutreachviasocialmedia,” Front.Sociol.,vol. 8, 2023

    2023

  40. [40]

    Detection and characterization of coordinated online behavior: A survey,

    L. Mannocci, M. Mazza, A. Monreale, M. Tesconi, and S. Cresci, “Detection and characterization of coordinated online behavior: A survey,” arXiv preprint arXiv:2505.10867, 2025

    work page arXiv 2025

  41. [41]

    Unsupervised detection of coordinated information operations in the wild,

    D. H. Smith, C. Ehrett, and P. L. Warren, “Unsupervised detection of coordinated information operations in the wild,” J. Online Trust Saf., vol. 1, no. 2, 2024

    2024

  42. [42]

    Lost in transformation: Rediscovering llm- generated campaigns in social media,

    B. Grimme J. Pohl, H. Winkelmann, L. Stampe and C. Grimme, “Lost in transformation: Rediscovering llm- generated campaigns in social media,” in Multidisciplinary Int. Symp. Disinformation in Open Online Media, Springer, 2023, pp. 72–87

    2023

  43. [43]

    Recom- mender systems and the amplification of extremist content,

    J. Whittaker, S. Looney, A. Reed, and F. Votta, “Recom- mender systems and the amplification of extremist content,” Internet Policy Review, 2023

    2023

  44. [44]

    Preference Amplification in Recommender Systems,

    D. Kalimeris, S. Bhagat, S. Kalyanaraman, U. Weinsberg, “Preference Amplification in Recommender Systems,” Proc. Nat. Acad. Sci., 2021

    2021

  45. [45]

    Bigseiz Test Outcomes: Applying Ml Techniques for Fake News Identification and Comparing Sir and Seiz Epidemic Models on Twitter’s Temporal Network,

    K. Bozhiqi, “Bigseiz Test Outcomes: Applying Ml Techniques for Fake News Identification and Comparing Sir and Seiz Epidemic Models on Twitter’s Temporal Network,” IEEE Access, 2023

    2023

  46. [46]

    Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epi- demiological approach,

    M. I. Gurung, N. Agarwal, and E. Addai, “Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epi- demiological approach,” Social Network Analysis and Mining, Springer, 2018

    2018

  47. [47]

    Probabilistic Contagion and Models of Influ- ence,

    A. Karaka, “Probabilistic Contagion and Models of Influ- ence,” Weights & Biases, 2023

    2023

  48. [48]

    Hawkes processes for understanding heterogeneity in information propagation on Twitter,

    L. Wang, and L. Zhang, “Hawkes processes for understanding heterogeneity in information propagation on Twitter,” Front. Phys., 2022

    2022

  49. [49]

    Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter,

    K. Erk, and N. A. Smith, “Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter,” in Proc. 54th ACL 2016 Vol. 2, 2016, pp. 393–398

    2016

  50. [50]

    Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance,

    N. Kanakaris, H. Ping, X. Xiao, N. K. Ahmed, L. Luceri, E. Ferrara, P. Bogdan, “Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance,” ACM on Web, 2025, pp. 2651–2660

    2025

  51. [51]

    Modeling the spread of fake news on Twitter,

    T. Murayama, S. Wakamiya, E. Aramaki, and R. Kobayashi, “Modeling the spread of fake news on Twitter,” PLOS ONE, 2021

    2021

  52. [52]

    Hawkes Process for Under- standing the Influence of Pathogenic Social Media Accounts,

    H. Alvari and P. Shakarian, "Hawkes Process for Under- standing the Influence of Pathogenic Social Media Accounts," 2019 2nd International Conference on Data Intelligence and Security (ICDIS), 2019, pp. 36-42

    2019

  53. [53]

    Hawkes process model with a time-dependent background rate and its application to high-frequency financial data,

    T. Omi, Y. Hirata, and K. Aihara, “Hawkes process model with a time-dependent background rate and its application to high-frequency financial data,” Phys. Rev. E, 2017

    2017

  54. [54]

    Location and Language Independent Fake Rumor Detection Through Epi- demiological and Structural Graph Analysis of Social Con- nections.,

    D. Serpanos, G. Xenos, and B. Tsouvalas, “Location and Language Independent Fake Rumor Detection Through Epi- demiological and Structural Graph Analysis of Social Con- nections.,” Front Artif Intell, 2022

    2022

  55. [55]

    Digital media and misinformation: An outlook on multidisciplinary strategies against manipula- tion,

    D. Caled and M. J. Silva, “Digital media and misinformation: An outlook on multidisciplinary strategies against manipula- tion,” PubMed Central, 2021, pp. 123-159

    2021

  56. [56]

    Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model,

    S. Perveen, M. Shahbaz, S. S. Albouq, K. Shinan, H. E. Alhazmi, F. Alanazi, “Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model,” PLOS ONE, 2025

    2025

  57. [57]

    A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion,

    M. Al-alshaqi, D. B. Rawat, and C. Liu, “A BERT-Based Multimodal Framework for Enhanced Fake News Detection Using Text and Image Data Fusion,” MDPI Computers, 2025

    2025

  58. [58]

    Modeling Misinformation Diffusion in Social Media: Beyond Network Properties ,

    F. Spezzano, “Modeling Misinformation Diffusion in Social Media: Beyond Network Properties ,” 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), 2021, pp. 168-171

    2021

  59. [59]

    Enhancing fake news detection with transformer-based deep learning: A multidisciplinary approach,

    N. Raza, S. J. Abdulkadir, Y. A. Abid, S. S. Albouq, A. Alwadain, A. U. Rehman, E. H. Sumiea, and M. Farhan, “Enhancing fake news detection with transformer-based deep learning: A multidisciplinary approach,” PLOS ONE, 2025

    2025

  60. [60]

    Temporal and Social ContextbasedBurstDetectionfromFolksonomies,

    J. Yao, B. Cui, Y. Huang, and X. Jin, “Temporal and Social ContextbasedBurstDetectionfromFolksonomies,” inAAAI, 2020, pp. 1474–1479

    2020

  61. [61]

    Bursty and hierarchical structure in streams ,

    J. Kleinberg, “Bursty and hierarchical structure in streams ,” ACM KDD, 2002, pp. 91-101

    2002

  62. [62]

    Propagation Structure-Aware Graph Transformer for Robust and Inter- pretable Fake News Detection,

    J. Zhu, C. Gao, Z. Yin, X. Li, and J. Kurths, “Propagation Structure-Aware Graph Transformer for Robust and Inter- pretable Fake News Detection,” ACM KDD, 2024, pp. 4652- 4663

    2024

  63. [63]

    Coordinated inauthentic behavior: An innova- tive manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media,

    M. Murero, “Coordinated inauthentic behavior: An innova- tive manipulation tactic to amplify COVID-19 anti-vaccine communication outreach via social media,” Frontiers in soci- ology vol. 8, 2023

    2023

  64. [64]

    Unsupervised detection of coordinated information operations in the wild,

    D. H. Smith, C. Ehrett, and P. Warren, “Unsupervised detection of coordinated information operations in the wild,” EPJ Data Science, 2025

    2025

  65. [65]

    Beyond Interaction Patterns: Assessing Claims of Coordinated Inter-StateInformation Operations on Twitter/X,

    V. Pantè, D. Axelrod, A. Flammini, F. Menczer, E. Ferrara, and Luca Luceri, “Beyond Interaction Patterns: Assessing Claims of Coordinated Inter-StateInformation Operations on Twitter/X,” ACM on Web Conferences, 2025, pp. 1234-1238

    2025

  66. [66]

    Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework,

    C. Wissuchek, and P. Zschech, “Exploring Agentic Artificial Intelligence Systems: Towards a Typological Framework,” (Pacific Asia Conference on Information Systems (PACIS), 2025

    2025

  67. [67]

    Advancing Agentic Systems: Dynamic Task Decomposition, Tool Inte- gration and Evaluation using Novel Metrics and Dataset ,

    S. K. Jeyakumar, A. Ahmad, and A. Gabriel, “Advancing Agentic Systems: Dynamic Task Decomposition, Tool Inte- gration and Evaluation using Novel Metrics and Dataset ,” NeurIPS Workshop on Open-world Agents, 2024

    2024

  68. [68]

    Multi-agent Systems for Misinformation Life- cycle : Detection, Correction And Source Identification,

    A. Gautam, “Multi-agent Systems for Misinformation Life- cycle : Detection, Correction And Source Identification,” ICWSM, 2025

    2025

  69. [69]

    MultiAgent Collaboration Attack: In- vestigating Adversarial Attacks in Large Language Model Collaborations via Debate,

    A. Amayuelas, X. Yang, A. Antoniades, W. Hua, L. Pan, and W. Y. Wang, “MultiAgent Collaboration Attack: In- vestigating Adversarial Attacks in Large Language Model Collaborations via Debate,” EMNLP, 2024

    2024

  70. [70]

    Improving Multi-Agent Debate with Sparse Communication Topology ,

    Y.Li,Y.Du,J.Zhang,L.Hou,P.Grabowski,Y.Li,andE.Ie, “Improving Multi-Agent Debate with Sparse Communication Topology ,” EMNLP, 2024, pp. 7281-7294

    2024

  71. [71]

    The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News,

    Y. Liu, Y. Liu, X. Zhang, X. Chen, and R. Yan, “The Truth Becomes Clearer Through Debate! Multi-Agent Systems with Large Language Models Unmask Fake News,” ACM, 2025, pp. 504-514

    2025

  72. [72]

    From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge,

    D. Li, B. Jiang, L. Huang, A. Beigi, C. Zhao, Z. Tan, A. Bhattacharjee, Y. Jiang, C Chen, and T. Wu, “From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge,” EMNLP, 2025

    2025

  73. [73]

    Large Language Models are not Fair Evaluators,

    P. Wang, L. Li, L. Chen, Z. Cai, D. Zhu, B. Lin, Y. Cao, L. Kong, Q. Liu, T. Liu, and Z. Sui., “Large Language Models are not Fair Evaluators,” ACL, 2024, pp. 9440-9450

    2024

  74. [74]

    LLM-as- a-Judge: Rapid Evaluation of Legal Document Recommenda- tion via Retrieval-Augmented Generation,

    A. Pradhan, A. Ortan, A. Verma, and M. Seshadri, “LLM-as- a-Judge: Rapid Evaluation of Legal Document Recommenda- tion via Retrieval-Augmented Generation,” RecSys, 2025

    2025

  75. [75]

    Limitations of the LLM-as-a- Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks,

    A. Szymanski, N. Ziems, H. A. Eicher-Miller, T. J. Li, M. Jiang, and R. A. Metoyer, “Limitations of the LLM-as-a- Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks,” ACM, 2025, pp. 952 - 966

    2025

  76. [76]

    Agent-as-a-Judge: Evaluate Agents with Agents,

    M. Zhuge, C. Zhao, D. Ashley, W. Wang, D. Khizbullin, Y. Xiong, Z. Liu, E. Chang, R. Krishnamoorthi, Y. Tian, Y. Shi, V. Chandra, and J Schmidhuber, “Agent-as-a-Judge: Evaluate Agents with Agents,” ICML Poster, 2025

    2025

  77. [77]

    Paraphrase diversification using counterfactual debiasing,

    S. Park, S. Hwang, F. Chen, J. Choo, J. Ha, S. Kim, and J. Yim, “Paraphrase diversification using counterfactual debiasing,” AAAI, 2019, pp. 6883-6891

    2019

  78. [78]

    Acharya, A

    T. Acharya, A. Annamalai and M. F. Chouikha, “Addressing the Class Imbalance Problem in Network-Based Anomaly VOLUME 1, 2026 13 Authoret al.: Generative AI and Digital Ecosystem Resilience: A Proactive Lifecycle-Based Survey Detection,” IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), 2024, pp. 1-6

    2026

  79. [79]

    Attacking Misinfor- mation Detection Using Adversarial Examples Generated by Language Models,

    P. Przybyła, E. McGill, and H. Saggion, “Attacking Misinfor- mation Detection Using Adversarial Examples Generated by Language Models,” EMNLP, 2025, pp. 27614–27630

    2025

  80. [80]

    Blockchain-Aided Secure Semantic Communication for AI- Generated Content in Metaverse,

    Y. Lin, H. Du, D. Niyato, J. Nie, J. Zhang, and Y. Cheng, “Blockchain-Aided Secure Semantic Communication for AI- Generated Content in Metaverse,” IEEE Open Journal of the Computer Society, 2023, pp. 72-83

    2023

Showing first 80 references.