Digital mental health interventions increasingly require robust security frameworks, authenticated content delivery, and personalized therapeutic experiences. This systematic literature review examines the convergence of Blockchain technology, non-fungible tokens (NFTs), and deepfake synthesis in mental health applications, addressing a critical gap in interdisciplinary research. Following PRISMA guidelines, we conducted comprehensive searches across Scopus database (2014-2024), supplemented by IEEE Xplore, Web of Science, and PubMed Central. Our methodology employed PICO framework-based queries, identifying 15 037 relevant studies across seven queries (Q) configurations (Q1-Q7), with 5093 studies meeting inclusion criteria after rigorous quality assessment. Results demonstrate Blockchain provides immutable data governance (3962 studies), NFTs enable secure therapeutic asset tokenization (771 studies), while deepfakes facilitate personalized avatar-based therapy (230 studies). Cross-technology integration remains limited: Blockchain-NFTs combinations (102 studies), Blockchain-Deepfake integrations (24 studies), NFTs-Deepfake applications (2 studies), with only 2 studies addressing all three technologies simultaneously. This review establishes the first comprehensive taxonomy of converged technologies in mental health, identifying critical research directions for scalable, secure, and ethically-compliant digital therapeutic platforms.
Dec 1, 2025·International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences
sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref
In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.
Ye Tian, Liangliang Song, Peng Qian, Yanbin Wang · 6 authors
The detection of malicious accounts on Ethereum - the preeminent DeFi platform - is critical for protecting digital assets and maintaining trust in decentralized finance. Recent advances highlight that temporal transaction evolution reveals more attack signatures than static graphs. However, current methods either fail to model continuous transaction dynamics or incur high computational costs that limit scalability to large-scale transaction networks. Furthermore, current methods fail to consider two higher-order behavioral fingerprints: (1) direction in temporal transaction flows, which encodes money movement trajectories, and (2) account clustering, which reveals coordinated behavior of organized malicious collectives. To address these challenges, we propose DiT-SGCR, an unsupervised graph encoder for malicious account detection. Specifically, DiT-SGCR employs directional temporal aggregation to capture dynamic account interactions, then coupled with differentiable clustering and graph Laplacian regularization to generate high-quality, low-dimensional embeddings. Our approach simultaneously encodes directional temporal dynamics, global topology, and cluster-specific behavioral patterns, thereby enhancing the discriminability and robustness of account representations. Furthermore, DiT-SGCR bypasses conventional graph propagation mechanisms, yielding significant scalability advantages. Extensive experiments on three datasets demonstrate that DiT-SGCR consistently outperforms state-of-the-art methods across all benchmarks, achieving F1-score improvements ranging from 3.62% to 10.83%.
One of many different application areas of the blockchain technology is crypto-currencies. Products like Bitcoin and Solana provide financial services that are unmediated, distributed and anonymous. Among various blockchains, Ethereum stands out due to its support of smart contracts. However, softly authenticated transactions occuring on such platforms facilitate crimes like money laundering and sales of illegal items/services. Denanonymization, over blockchains, refers to identifying distinct accounts of the same person and is used for tracking illegal trafficking of cryptocurrencies. In this study, our purpose is to increase the rate of success of deanonymization and to support explainable approaches. Towards this aim, we imitate blockchain analysts and propose 19 novel heuristic features that are volumetric and temporal. Empirical experiments indicate that temporal features increase the attack success rate by 39%. Shapley values adapted from the cooperative game theory field support this finding.
Moein Shahiki Tash, Olga Kolesnikova, Zahra Ahani, Grigori Sidorov
This paper provides an extensive examination of a sizable dataset of English tweets focusing on nine widely recognized cryptocurrencies, specifically Cardano, Binance, Bitcoin, Dogecoin, Ethereum, Fantom, Matic, Shiba, and Ripple. Our goal was to conduct a psycholinguistic and emotional analysis of social media content associated with these cryptocurrencies. Such analysis can enable researchers and experts dealing with cryptocurrencies to make more informed decisions. Our work involved comparing linguistic characteristics across the diverse digital coins, shedding light on the distinctive linguistic patterns emerging in each coin's community. To achieve this, we utilized advanced text analysis techniques. Additionally, this work unveiled an understanding of the interplay between these digital assets. By examining which coin pairs are mentioned together most frequently in the dataset, we established co-mentions among different cryptocurrencies. To ensure the reliability of our findings, we initially gathered a total of 832,559 tweets from X. These tweets underwent a rigorous preprocessing stage, resulting in a refined dataset of 115,899 tweets that were used for our analysis. Overall, our research offers valuable perception into the linguistic nuances of various digital coins' online communities and provides a deeper understanding of their interactions in the cryptocurrency space.
Zheyuan He, Zihao Li, Sen Yang, Ye, He · 8 authors
Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. Notably, recent studies are increasingly exploring LLMs applied to the context of blockchain security (BS). However, there remains a gap in a comprehensive understanding regarding the full scope of applications, impacts, and potential constraints of LLMs on blockchain security. To fill this gap, we undertake a literature review focusing on the studies that apply LLMs in blockchain security (LLM4BS). Our study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems. Through a thorough examination of existing literature, we delve into the integration of LLMs into various aspects of blockchain security. We explore the mechanisms through which LLMs can bolster blockchain security, including their applications in smart contract auditing, transaction anomaly detection, vulnerability repair, program analysis of smart contracts, and serving as participants in the cryptocurrency community. Furthermore, we assess the challenges and limitations associated with leveraging LLMs for enhancing blockchain security, considering factors such as scalability, privacy concerns, and ethical concerns. Our thorough review sheds light on the opportunities and potential risks of tasks on LLM4BS, providing valuable insights for researchers, practitioners, and policymakers alike.
The metaverse and non-fungible tokens (NFTs) were some of the hottest tech terms in 2021, according to a Google Trends search. Our review aims to describe the metaverse and NFTs in the context of their potential application in the treatment of mental health disorders. Advancements in technology have been changing human lives at an ever-increasing pace. Metaverse, also known as the three-dimensional (3D) internet, is the convergence of virtual reality (VR) and physical reality in a digital space. It could potentially change the internet as we know it, with NFTs as the key building blocks in the new expansive virtual ecosystem. This immersive 3D virtual world boasts the features of the real world with the added ability to change the surrounding environment according to individual needs and requirements. VR, augmented reality (AR) and mixed reality (MR) have been employed as tools in the treatment of various mental health disorders for the past decade. Studies have reported positive results on their effectiveness in the diagnosis and treatment of mental health disorders. VR/AR/MR have been hailed as a solution to the acute shortage of mental health professionals and the lack of access to mental healthcare. But, on the flip side, young adults tend to spend a significant amount of time playing 3D immersive games and using social media, which can lead to insecurity, anxiety, depression, and behavioural addiction. Additionally, endless scrolling through social media platforms negatively affects individuals9 attention span as well as aggravating the symptoms of adolescents with attention deficit hyperactivity disorder. We aimed to explore the ramifications of expanding applications of the metaverse on mental health. So far, no other review has explored the future of mental health in the context of the metaverse.