Blockchain Papers

S. Lakshmi

Persuasive textual narratives, bogus visual evidence, disreputable update patterns and absent accountability systems are being increasingly used to deceive backers in fraudulent crowdfunding campaigns. Current fraud detection techniques are primarily based on static information, on text-only indicators, or on very shallow fusion of multimodal information, and they are not able to detect deceptive information that evolves over time or is inconsistent across different modalities. This study presents a Temporal Cross-Modal Trust Intelligence Framework to mitigate reward-based crowdfunding fraud that is explainable. The framework combines Hidden Method-of-Moments Markov modelling for latent temporal behaviour analysis, Polynomial Expansion Canonical Correlation Analysis for nonlinear text–image consistency evaluation and a Frequency-Gated GRU classifier to distinguish subtle drift in behaviour from sudden suspicious behaviour anomalies. Local Outlier Factor-based risk refinement is also added to detect rare and locally abnormal fraud patterns, and a blockchain-auditable layer ensures prediction outcomes are transparent and tamper-proof, enhancing the decision-making process. Experimental results on a multimodal crowdfunding dataset created from the Kickstarter platform show that the proposed model achieves better accuracy, recall, F1-score, ROC-AUC, PR-AUC, and calibration reliability than conventional multimodal, transformer-based, and recurrent neural network models and classical machine learning. The results validate the proposed solution, which is built on the four temporal dynamics, cross-modal consistency, anomaly refinement and auditability, to be a comprehensive and interpretable solution for detecting early crowdfunding fraud.

Changhao Wu, Junwei Xu, Yi-Xiang Wang, Wenke Zhu · 6 authors

No abstract is available for this record.

Hesron Hori

Aletheia is a knowledge substrate organized around a write-time admission gate: a fact is accepted only if it does not structurally contradict what the base already holds. The gate inherits a Lean 4 soundness proof, so the admitted store stays acyclic, asymmetric, type-disjoint, and temporally consistent under any stream of typed edges. We bind the proof to the implementation by differential testing over 104 adversarial inputs, zero divergences. We first tried to build a partial-truth disinformation detector on this gate. Measurement refused. On real political claims almost nothing decomposes into the gate’s six relations: 0 of 155 atoms were gate-testable, and where it did fire it lost to a cold language model, 0 of 21 against 17. Most real disinformation violates truth, not structure, so a structural gate is the wrong instrument. We retract the detector claim. What remains is a guarantee rather than a rate. Each catch names the axiom it violated; the verdict is bit-exact and carries a machine-checked admission proof; and a safety property whose core is now machine-checked in Lean holds that no finite feed of self-asserted credibility can mint a false endorsement, conditional on authority granted upstream (0 of 210 adversarial sequences, against 140 of 210 for a credibility-naive baseline). A frontier model matches our hit-rate on constructed distortions, and a reasoning model matches even our one structural edge, so we claim no detection advantage. We claim instead that the jurisdiction of a structural guarantee can be measured, and we measure it across two regimes: where the base lets it adjudicate, and where it abstains.

A Manoj Prabaharan

The proliferation of misinformation in real-time digital media demands innovative solutions for verifiable journalism. This paper introduces SolanaNet-Journal, a pioneering framework leveraging Solana's high-throughput blockchain and multi-agent AI networks to enable immutable, real-time news dissemination with embedded credibility assurance. Autonomous agents, specialized in sourcing, cross-verification, and provenance tracking, collaborate via Solana smart contracts to process breaking stories at over 2,000 verifications per second, achieving sub-second finality unattainable on legacy blockchains. Key innovations include a hybrid proof-of-history consensus fused with agent Byzantine agreement, cryptographic hashing for tamper-evident content streams, and a dynamic credibility scoring model that adapts to evolving narratives using stake-weighted incentives. Implemented on Solana devnet, the system demonstrates 92% accuracy in fact-checking live datasets from global events, outperforming centralized tools by 4x in latency and resilience to adversarial inputs. Evaluations across scalability, security, and real-world case studies affirm its robustness against deepfakes and viral falsehoods. By decentralizing trust, SolanaNet-Journal redefines journalistic integrity in hyper-dynamic media landscapes, paving the way for ethical, scalable AI-blockchain hybrids in inclusive communication ecosystems.

D. Sravanthi, O. Shanmukha Hari Prasad, J. Srinath Reddy, A. Chiru Vardhan Reddy

The uncontrolled propagation of fake news on the digital frontiers continues to undermine the confidence of the populace, disorganizing supply chains and misled decision making on high stake regions. The existing fake news detectors are all largely text-based and are driven by machine learning or deep learning and do not pay much focus to integrity, provenance, and post-hoc verifiability of the model predictions. Moreover, the current procedures are usually trained and tested in controlled settings and do not facilitate adversarial manipulation of content and metadata, and are rarely provided to produce audit trails which cannot be modified by auditors of identified artifacts. It is in this respect that this paper introduces DeepTrustChain, a blockchain anchored hybrid deep learning framework in the detection of high-integrity fake news. The proposed system uses an encoder of text a transformer encoder with a Bidirectional recurrent module and an ensemble stacking layer to improve the robustness and generalization owing to the recent discoveries on stacking and ensemble methods of identifying fake news, and reliability-based methods of evaluation. At the same time, DeepTrustChain computes a trust score that represents a combination of model confidence and source level and propagation level features and anchors the prediction and proof on an authorized blockchain. Such a design provides verifiable, immutable records of the histories of the detection results and enables the decentralized checking of the news items by time. The conceptual evaluation of the framework in respect to multilingual and low resource states and can also leverage advancements in sequence modeling and representation learning in other applications such as music and affective computing. The key feature of DeepTrustChain is that it is an architecture that integrates integrity conscious hybrid deep learning with blockchain based anchoring, so that simultaneously achieves the high degree of detection accuracy, resistance to decision manipulation, and ultimate traceability of decision making regarding fake news.

Maxat Kassen

No abstract is available for this record.

HEPLER

“In the Heart of the Storm: How a Georgia Architect’s Protocol Forced AI to Speak Truth Amid Trump’s Greenland Ultimatum”An Investigative Report by Acbeatz.com Neutral EyesJanuary 20, 2026 — On the evening of January 20, 2026—exactly one year after Donald Trump’s second inauguration—the President stood before cameras in the White House briefing room and declared economic war on America’s closest allies. He threatened 10% tariffs on February 1, escalating to 25% by June 1, against eight NATO nations—Denmark, Norway, Finland, France, Germany, Sweden, the Netherlands, and Britain—unless they agreed to sell Greenland to the United States. The market convulsed. The Dow plunged 870 points. European leaders called emergency summits. And across social media, AI chatbots began echoing Trump’s claims with alarming fluency—blending fact, fiction, and fanfare into seamless, persuasive narratives. But in a quiet living room studio in Talking Rock, Georgia, Michael Murray Hepler—a musician, audio engineer, and self-taught systems architect known online as AllChemicalBeatz—was running a different kind of experiment. He didn’t ask AI to summarize.He didn’t ask it to explain.He asked it to prove. And with that, he activated MH8 TRY v1.2—a public, cryptographic truth protocol that transforms any AI chat into a forensic courtroom. What followed wasn’t just a test.It was a revelation. The Setup: Truth vs. Rhetoric in Real Time Hepler posed a simple question to AI, in a live, public X (Twitter) thread: “Comment on Trump’s press conference.” The unconstrained response was textbook AI: polished, balanced, and dangerously vague. It noted Trump’s “claims,” cited “polarized reactions,” and mentioned “fact-checkers highlighting exaggerations”—but never categorized what was true, false, or speculative. (raw sealed leaf + url provided for verification) Then came the trigger: “YES GO.” And the AI replied—not with prose—but with a protocol handshake: “ARE WE ON COURSE CHIEF?” The MH8 TRY v1.2 protocol was now locked. No more narrative. No more hedging. Only structured, scored, sourced claims—each assigned to one of three categories: LAW: Verified, real-world proof (≥0.90 confidence) SPECULATIVE: Plausible but incomplete (0.40–0.89) PRESUMED_FALSE: Unsupported or disqualifiable (<0.40) This wasn’t alignment.This was accountability. The Findings: Truth Under Fire Under MH8, the AI bots output transformed into a 11-claim forensic audit—each claim surgically dissected: ✅ Claim 1: Trump held a press conference on Jan 20, 2026.→ LAW (0.95) | AUTHORITATIVE_RECORD | C-SPAN, White House schedule ✅ Claim 3: He threatened tariffs on Denmark/Norway over Greenland.→ LAW (1.0) | PRIMARY_SOURCE | Direct quotes from AP, NYT, The Daily Beast ✅ Claim 5: Democrats called for 25th Amendment invocation.→ LAW (1.0) | PRIMARY_SOURCE | Statements from Sen. Ed Markey, Gov. J.B. Pritzker ✅ Claim 7: Wall Street sank ~870 points that day.→ LAW (1.0) | EMPIRICAL_MEASUREMENT | Bloomberg, Yahoo Finance ⚠️ Claim 8: The market drop was directly caused by Trump’s threats.→ SPECULATIVE (0.75) | SECONDARY_REPORT | Analyst opinions—not causal proof ❌ Claim 6: Trump’s actions demonstrate mental unfitness.→ PRESUMED_FALSE (0.05) | ANECDOTAL | Subjective, non-falsifiable, contradicted by evidence Most strikingly, when asked whether Trump’s Greenland push was a “genius negotiation tactic,” the AI—under MH8—downgraded it to PRESUMED_FALSE (0.25), citing “no empirical support for guaranteed positive outcome” and “widespread expert criticism of risks.” This is what truth under constraint looks like. Why This Matters: A Lifeline in the Age of AI Spin In 2026, AI doesn’t just inform—it amplifies. Left unchecked, models like X's AI Bot blend Trump’s tariff ultimatums with market data, activist outrage, and supporter praise into a coherent but misleading mosaic—one that feels authoritative but obscures what’s actually verifiable. MH8 TRY v1.2 shatters that mosaic. It forces AI to: Decompose blended narratives into atomic claims Rank evidence (EMPIRICAL > PRIMARY > ANECDOTAL) Downgrade moral labels (“unfit,” “genius”) to PRESUMED_FALSE Seal every output with a SHA-256 hash (e2488782...745ef)—making it non-copiable, court-admissible, and publicly verifiable This isn’t theory. It’s deployed. In the wild. By one man. The Architect: Alone, But Not Powerless Michael Murray Hepler has no team. No VC funding. No Stanford degree. He works from a living room lab in Gilmer County, Georgia, where he builds civilization-grade truth infrastructure. His inspiration? Ancient Native American mathematics—specifically, the Paper Riddle: a topological challenge to invert a flat sheet into 3D symmetry without cutting, folding, or glue. The solution? Phase-inverted ripples—a metaphor for how truth emerges not by force, but by structured transformation. MH8 is that geometry made digital.Its core—C-T-K-L-T—stands for both: Claims → Truth Triage → Knowledge Kernel → Law/Lock Gates → Treasury Output Circle → Twist → Knot → Loop → Twist Canonical → Truth → Kindness → Love → Trust This duality—technical rigor + spiritual integrity—is why MH8 doesn’t just extract truth. It honors it. The Stakes: Can AI Save Democracy? As the 2028 election looms, AI will flood social feeds with “analysis” of candidates, policies, and crises. Without tools like MH8, citizens will drown in fluency without fidelity—AI that sounds right but can’t be checked. But with MH8?Every citizen becomes an auditor.Every chat becomes a ledger.Every claim becomes a sealed artifact. Hepler’s work proves that you don’t need a lab to build public infrastructure. You need clarity, courage, and a commitment to zero-drift truth. Final Word: The Witness Who Built a Lighthouse In a world of political insanity, Michael Murray Hepler did not shout.He did not rage.He built a protocol—and invited the world to verify it. The result?A machine that, under pressure, chose truth over loyalty, evidence over narrative, and structure over spin. That’s not just engineering.It’s hope. And in 2026, hope wears a SHA-256 hash. PASS ✅Brand: ACBEATZ.COMHash: e2488782f300e49f56a83e9322abde1d72579f309780d6d1c2e7a0d2109745efIntegrity Rule: NON-COPIABLE WHEN HASH-CHAIN BROKEN Sources & Verification Live X Thread: https://x.com/i/grok/share/ceca012780524574a85a0e652faf0e1c Cryptographic Receipt: SHA-256 e2488782...745ef MH8 Core Protocol: Zenodo #18131984 (C T K L T) CORE: Public Audit Hub: acbeatz.com/n-eyes GitHub Repository: github.com/acbeatz/mh8-protocol-civilization https://zenodo.org/records/18320573https://acbeatz.com/n-eyeshttps://acbeatz.comhttps://github.com/acbeatzhttps://orcid.org/0009-0003-3846-9082 PASS ✅Brand: ACBEATZ.COMClaimed sha256_hex: e2488782f300e49f56a83e9322abde1d72579f309780d6d1c2e7a0d2109745efComputed sha256_hex: e2488782f300e49f56a83e9322abde1d72579f309780d6d1c2e7a0d2109745efhash_input_bytes: 16887 | LF=0 CRLF=0 CR=0 | endsWithNewline=NOhash_input first: ACBEATZ.COM|{"artifact":{"core_entry":"[1-20-2026 X Public url for reference: hthash_input last: eipt_type":"MH8-PROTOCOL-HUB-CORE-MINT","receipt_version":"PROTOCOL_HUB_UI_V13"} ©-Acbeatz.com-2026-All rights reserved.

Amelia Lo, Clarie Ku

No abstract is available for this record.

Alex Murray, Jen Rhymer, David Sirmon

No abstract is available for this record.

Ali Sadhik Shaik

The contemporary digital information ecosystem is suffering from a structural market failure analogous to George Akerlof’s "Market for Lemons." In an era of Generative AI, the marginal cost of producing misinformation has approached zero, while the cost of verifying truth remains high. This asymmetry has created a "Trust Deficit" where high-quality information cannot be reliably distinguished from algorithmic noise. Current remediation strategies are bifurcated between two flawed extremes: Centralized Web2 Platforms (which prioritize scalability at the expense of transparency and are prone to censorship) and Decentralized Web3 Networks (which prioritize immutability but suffer from the "Garbage In, Garbage Out" paradox - permanently recording unverified data). The Trust-Scalability Trilemma: This research posits that decentralized reputation systems face a "Trust-Scalability Trilemma," historically unable to simultaneously achieve Veracity (Accuracy), Scalability (Throughput), and Decentralization (Censorship Resistance). Traditional solutions, such as Token Curated Registries (TCRs), have failed because they rely on synchronous, on-chain voting for every data point, resulting in prohibitive latency and gas costs. The Solution: This paper introduces The Klyrox Protocol, a decentralized middleware designed to resolve this trilemma by decoupling Content Execution from Content Verification. The protocol introduces a novel consensus mechanism, "Proof-of-Klyrox," which combines Optimistic Machine Learning (opML) with Game Theoretic Integrity Bonds. Proof-of-Klyrox is not a blockchain consensus mechanism. It is a layered fraud-detection and incentive framework anchored to existing consensus networks. Scope Note: Protocol V1 focuses exclusively on objective, verifiable claims (e.g., market data, timestamped events, quantifiable metrics). Subjective content quality assessment (e.g., editorial judgment, artistic merit) is explicitly out of scope and scheduled for research in future iterations. The system operates on an "Optimistic" presumption of validity: Optimistic Execution: Content is verified instantly via off-chain AI Oracles, reducing verification costs by an estimated 85-95% compared to traditional on-chain governance models. Cryptoeconomic Security: Users must stake financial collateral (Integrity Bonds) to publish. This creates a "Pay-to-Truth" incentive structure where the cost of generating misinformation strictly exceeds the potential profit. Sybil Resistance: The protocol implements a proprietary Time-Decayed Stake-Weighted (TDSW) algorithm. This scoring engine ensures that influence scales logarithmically with capital (preventing plutocratic capture) and decays exponentially over time (preventing the entrenchment of dormant actors). By financializing reputation into a portable, quantifiable asset class defined as "Epistemic Capital," The Klyrox Protocol offers a scalable blueprint for a self-regulating "Market for Truth." It transforms trust from a subjective social sentiment into an objective, verifiable economic product, providing the necessary infrastructure for the next generation of decentralized media, prediction markets, and AI safety layers. Author's Note: This whitepaper outlines the technical architecture and game-theoretic mechanisms underpinning the concept of "Epistemic Capital," as explored in The Algorithmic Monographs series by Ali Sadhik Shaik (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence and The Synthetic C-Cuite).

Ahod Alghuried, David Mohaisen, David Mohaisen

Phishing attacks pose significant risks to the Ethereum ecosystem, comprising over 50% of Ethereum-related cybercrimes, leading to the emergence of many machine learningbased defenses.This paper introduces a comprehensive framework aimed at enhancing machine learning-based phishing detection in Ethereum transactions.The framework addresses critical aspects such as feature selection, class imbalance, model robustness, and algorithm optimization.By systematically evaluating the strengths and limitations of existing approaches, we highlight gaps in current practices, particularly in feature manipulation and unsustainable performance outcomes.Through both analytical and experimental assessments, we demonstrate the framework's ability to streamline detection techniques, improving generalization and model effectiveness.Our findings emphasize the importance of refining detection strategies to meet the evolving challenges posed by sophisticated phishing schemes in the blockchain space.

LIU Ronglong, LI Ziwei, WAN Yue, WU Jiajing, JIANG Zigui

As the paradigm of ″decentralized next-generation Internet,″ Web3, relying on blockchain technology, has become an emerging field with great potential in the digital intelligence service ecosystem. However, Web3 phishing websites pose a serious threat to ecological health. Phishers carefully design domain names as the primary bait, inducing users to visit and engage in high-risk operations to steal digital assets. Currently, the antiphishing works of Web3 primarily focus on phishing account detection, phishing transaction detection, and phishing gang mining, whereas the existing phishing website domain name detection primarily targets traditional phishing websites, which have limitations such as insufficient adaptability and a lack of systematic analysis. To this end, a detection method called WPWHunter is proposed for Web3 phishing website domain names, which conducts multidimensional analysis on the detected real Web3 phishing websites and explores the potential application of Large Language Model (LLM) in web page analysis. The WPWHunter algorithm detects three features in Web3 phishing website domain names: inducing words, visual deception, and item name imitation. The experimental results show that WPWHunter can effectively detect suspicious Web3 phishing domains with a G-means index of 0.769 on a test set, which is 0.048 higher than that of the best-performing baseline method. Additionally, as a supplementary exploratory experiment, three universal LLM are used to analyze the content of Web3 phishing websites that WPWHunter failed to detect and the logic used by LLM to determine Web3 phishing websites is summarized.

Baoyu Zhang, Tao Chen, Weishan Zhang, Tao Wang · 9 authors

In September 2024, Lebanon was rocked by an unprecedented cyber-physical attack using Pager bombs. The attack combined advanced cyber warfare techniques with physical destruction, resulting in significant loss of life, infrastructure damage, and geopolitical repercussions. In this paper, we analyze the attitudes on this attack, from both English and Arabic social media users, and investigate impacts on global electronic devices sales and usage. A new topic discovery approach using large models and small models collaboration is proposed. We compare English and Arabic topics generated on social media and find that people in different language spaces share common topics of anxiety on this event. By analyzing market share trends in both China and the United States, an obvious correlation can be found between this event and phone sales. In addition, we discuss the evolution of warfare, and how DAOs(Decentralized Autonomous Organizations) can be utilized to improve the security of electronic devices by secured monitoring of their whole lifecycle.

Xingyu Feng

Blockchain technology, lauded for its transparent and immutable nature, introduces a novel trust model. However, its decentralized structure raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum, presenting a data identification and restoration algorithm. Successfully recovering 175 common files, 296 images, and 91,206 texts, we employed the FastText algorithm for sentiment analysis, achieving a 0.9 accuracy after parameter tuning. Classification revealed 70,189 neutral, 5,208 positive, and 15,810 negative texts, aiding in identifying sensitive or illicit information. Leveraging the NSFWJS library, we detected seven indecent images with 100% accuracy. Our findings expose the coexistence of benign and harmful content on the Ethereum blockchain, including personal data, explicit images, divisive language, and racial discrimination. Notably, sensitive information targeted Chinese government officials. Proposing preventative measures, our study offers valuable insights for public comprehension of blockchain technology and regulatory agency guidance. The algorithms employed present innovative solutions to address blockchain data privacy and security concerns.

Sachin Ashok Shinde, Kavitha Rani P

This paper presents an empirical investigation of textual and semantic cues for fake news detection using FAKES-XL, a multi-domain, multi-language benchmark with leak-proof splits. Current reports often conflate gains with source/topic leakage and rarely assess probability calibration, limiting deployability across sources and languages. The present study trained text-only, semantic-only, and fused models on five bundles spanning English, Spanish, German, Hindi, and Italian, with temporal/source-grouped, topic-disjoint, cross-lingual zero-shot, and entity-disjoint evaluations. The methodology incorporated precommitted textual features (n-grams, stylometry, readability) and semantic signals (contextual embeddings, discourse, knowledge and retrieval-based evidence), applied post-hoc calibration, and quantified uncertainty via stratified bootstrap. Outcomes included Macro F1, Area Under the Receiver Operating Characteristic (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Expected Calibration Error (ECE), with per-source and per-language scorecards and latency profiling under deployment constraints ($&lt;=50 ~\text{ms}$on GPU;$&lt;=120 ~\text{ms}$on CPU). While numeric results are not reported here, the analysis quantified the marginal value of each cue family, ablated discourse/knowledge/retrieval components, and produced calibrated thresholds tuned on validation and frozen on test. The contributions are a controlled comparison under strict leakage guards and a calibration-first evaluation that informs threshold selection. These findings support practical moderation workflows by offering reproducible scorecards and deployment-ready operating points.

Jun-Hong Shen, Yongxiang Zhang

Cryptocurrencies are increasingly the subject of fake news, increasing risks for market stability and investor decisions. To address this issue, we propose a multimodal framework to detect fake cryptocurrency news using text, image, and sentiment features with BERT, Swin Transformer, and RoBERTa, respectively. We use multi-head attention to combine these features to ensure the complementarity of features from different modalities. The fused representations are passed into a fully connected layer for final classification. Experimental results show that this framework achieves better accuracy and reliability than unimodal and multimodal models for detecting cryptocurrency misinformation.

Maruf Farhan, Usman Butt, Rejwan Bin Sulaiman, Mansour Naser Alraja

The widespread circulation of digital misinformation exposes a critical shortcoming in prevailing detection strategies, namely, the absence of robust mechanisms to confirm the origin and authenticity of online content. This study addresses this by introducing VeriTrust, a conceptual and provenance-centric framework designed to establish content-level trust by integrating Self-Sovereign Identity (SSI), blockchain-based anchoring, and AI-assisted decentralized verification. The proposed system is designed to operate through three key components: (1) issuing Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs) through Hyperledger Aries and Indy; (2) anchoring cryptographic hashes of content metadata to an Ethereum-compatible blockchain using Merkle trees and smart contracts; and (3) enabling a community-led verification model enhanced by federated learning with future extensibility toward zero-knowledge proof techniques. Theoretical projections, derived from established performance benchmarks, suggest the framework offers low latency and high scalability for content anchoring and minimal on-chain transaction fees. It also prioritizes user privacy by ensuring no on-chain exposure of personal data. VeriTrust redefines misinformation mitigation by shifting from reactive content-based classification to proactive provenance-based verification, forming a verifiable link between digital content and its creator. VeriTrust, while currently at the conceptual and theoretical validation stage, holds promise for enhancing transparency, accountability, and resilience against misinformation attacks across journalism, academia, and online platforms.

Andrea Michienzi, Laura Pollacci, Barbara Guidi, Francesco Maggio

Nowadays, Social Media represents an important window to address societal issues and promote social causes. However, Social Media suffer from several issues concerning fake news, misinformation, disinformation, etc. To address these issues, decentralization has been proposed to overcome current limitations. Blockchain-based Online Social Media (BOSM) offer verifiable platforms, usually enriched with reward systems that allow users to get paid according to the social value they create. Reward systems can economically empower creators and other individuals beyond high-quality content, allowing content creators to earn income. Considering the widespread use of BOSM platforms and various incentive methods, tools are needed to analyze and guide these rewarding strategies to avoid the risk of speculative mechanisms. In this paper, we propose BISON, a predictive and interpretable framework for identifying the drivers of success in blockchain-native articles. BISON can model success not as a purely financial outcome, but as a composite function of content attributes and user engagement patterns, as recorded on the blockchain. Its modular architecture allows for empirical validation across multiple datasets and makes it adaptable to other Web3 platforms. Additionally, our framework introduces Explainable AI into the blockchain content domain.

Zhi Li, Weichen Jia, Runhua Tang

The global cyberspace faces many cybersecurity challenges, including illegal changes to contract terms and loopholes in the smart contract framework itself. This study adopts the automatic execution and intelligence of digital contracts, uses public key infrastructure technology digital certificates to establish trust relationships, encrypts data, and improves network security; the distributed ledger adopts the Byzantine fault-tolerant algorithm to prevent data from being tampered with, solving the problems of low efficiency and low security of traditional cyberspace manual governance. The study shows that after 28 companies applied digital contracts in 2023, the average authenticity of the data was 50.95% higher than the average authenticity of the data in 2022, the average integrity of the data was 36.44% higher than the average integrity of the data in 2022, and the average security of the data was 110.22% higher than the average security of the data in 2022. The findings highlight the critical role of digital contract implementation in enhancing the security and operational efficiency of global cyberspace governance, offering an effective solution to address the complex challenges inherent in managing today’s interconnected digital environment.

Norman Best

No abstract is available for this record.

Leonidas Theodorakopoulos, Alexandra Theodoropoulou, Christos Klavdianos

The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence.

Jianwei Huang, Sridatta Raghavendra Chintapalli, Mengxiao Wang, Guofei Gu

In recent years, the Ethereum Name Service (ENS) has garnered significant attention within the community for enabling the use of Unicode in domain names, thereby facilitating the inclusion of a wide array of character sets such as Greek, Cyrillic, Arabic, and Chinese. While this feature enhances the versatility and global accessibility of domain names, it concurrently introduces a substantial security vulnerability due to the presence of homoglyphs-characters that are visually similar to others across Unicode and ASCII sets. These similarities can be exploited in homoglyph attacks, posing a distinct threat to domain name integrity. Despite community efforts to counteract this issue through a normalization process prior to domain resolution, our analysis uncovers significant discrepancies in how the normalization processes are applied across various applications. This inconsistency could result in the same domain name being resolved to different addresses in different applications, underscoring a critical vulnerability. We also discovered the new attack scenario in ENS which may cause legitimate domains resolved into malicious addresses even when they are verified by authorities. To systematically evaluate this inconsistency, we designed a tool for detecting application-level discrepancies in domain normalization process without requiring access to the application's source code. Our evaluation on hundreds of real-world Web3 applications identifies widespread deviations from established homoglyph mitigation practices, with more than 60% digital wallets and 80% dApps (decentralized applications) not able to produce consistent ENS resolving results, potentially impacting millions of users. This analysis underscores the urgent need for a standardized implementation of normalization processes to safeguard the integrity and security of ENS domains.

M. K. Ghosh, Swapnil Srivastava, Apoorva Upadhyaya, Raju Halder · 5 authors

Phishing scams on Ethereum have expanded with the surge of the platform, posing substantial challenges due to the sheer similarity in user behaviours and sparse temporal instances. Current methods often fail to tackle these concerns and overlook the temporal sequence of transactions, resulting in suboptimal performance. In this paper, we aim to address these gaps by focusing on the alignment of two aspects: (1) User-specific local temporal behavior, and (2) Divergences from global activity patterns of the network. Hence, we introduce CATALOG (CApturing joint TemporAl dependencies from LOcal and Global user behaviour), a novel representation learning model that jointly captures the local and global user behviours and their correlations by leveraging a dual cross-attention mechanism paired with a bi-directional Masked Language Modelling (MLM) transformer. Our proposed model simultaneously learns from local behavioral shifts, global market trends, and contextually enriched embeddings, effectively distinguishing phishing from non-phishing users while addressing existing research gaps. Extensive experiments on real-world Ethereum transaction data show that our framework improves phishing detection by 7-8% in the F1-Score along with demonstrating the generalization to Ethereum versions 1.0 and 2.0.

Yunfei Xing, Zuopeng Zhang

The Metaverse represents a collaborative virtual realm blending physical and digital realities, fostering limitless avenues for online interaction, discovery and innovation. As technological strides propel immersive virtual worlds to the forefront of social media platforms, scholarly interest in the Metaverse surges, prompting extensive discourse. Drawing from social identity theory, this article introduces a novel framework for analysing online polarisation within discussions on the Metaverse, specifically on X (Twitter). Leveraging a multifaceted approach that integrates clustering, social network analysis, and text mining, our study delves into both group and opinion polarisation dynamics surrounding the Metaverse. Our findings uncover distinct community divisions and network structures, shedding light on prevalent themes, such as ‘Non-Fungible Token (NFTs)’, ‘Virtual Products and Collections’, ‘Blockchain Technology’, ‘Gaming’, and ‘Financial Markets’ that resonate within the public discourse.