Olanrewaju Oluwaseun Ajayi, Chisom Elizabeth Alozie, Olumese Anthony Abieba, Joshua Idowu Akerele · 5 authors
Blockchain technology has emerged as a transformative force within the financial technology (Fintech) sector, offering unprecedented opportunities for efficiency, transparency, and security. However, its adoption also brings forth new challenges and vulnerabilities, particularly in the realm of cybersecurity. This review explores the dynamic landscape of Blockchain Technology and Cybersecurity in Fintech, highlighting both the opportunities it presents and the vulnerabilities it introduces. Blockchain technology, most notably recognized as the underlying framework for cryptocurrencies like Bitcoin and Ethereum, operates on a decentralized ledger system, enabling secure and immutable transactions. In Fintech, this technology promises enhanced transactional speed, reduced costs, and increased transparency, revolutionizing traditional banking and payment systems. Nevertheless, the decentralized nature of blockchain networks, while offering resilience against single points of failure, also poses unique cybersecurity risks. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, introduce vulnerabilities such as code bugs and exploits. Moreover, the anonymity associated with blockchain transactions has raised concerns regarding illicit activities, money laundering, and terrorist financing. In response to these challenges, the intersection of Blockchain Technology and Cybersecurity in Fintech offers opportunities for innovation. Advanced cryptographic techniques, such as multi-signature authentication and zero-knowledge proofs, are being leveraged to enhance security and privacy in blockchain-based systems. Additionally, regulatory frameworks are evolving to address the emerging risks associated with Fintech innovations, ensuring compliance and consumer protection. While Blockchain Technology presents promising opportunities for revolutionizing Fintech, its integration must be accompanied by robust cybersecurity measures to mitigate vulnerabilities and safeguard against potential threats. Collaborative efforts between industry stakeholders, regulators, and cybersecurity experts are imperative to foster a secure and resilient ecosystem for blockchain-based financial services.
Play-to-earn (P2E) games targeting users unfamiliar with cryptocurrencies are playing a key role within the industries known as blockchain, crypto or Web3. P2E gaming guilds (Elliott, 2021) are emerging as essential intermediaries bridging Web2 and Web3 ecosystems. Drawing from social network theory’s study of brokerage motivations, this paper examines the structure and communication practices of a P2E guild, Yield Guild Games (YGG) on Twitter. Through a computational analysis of YGG’s presence on Twitter, the paper compares two mention networks corresponding to a period of optimism, and a period of crisis. We used network analysis to examine the structure of YGG communication on Twitter (Rathnayake, 2023), analysed tweets using BERTopic topic modeling (Grootendorst, 2022), and extracted links to determine what information is shared within the network (Hoyng, 2023). The results demonstrate YGG’s role as a “cultural broker” (Foster & Ocejo, 2015) promoting the adoption of blockchain technologies, such as non-fungible tokens (NFTs) and ideologies (Swartz, 2017), ascribing legitimacy and value to particular actors and products in the Web3 ecosystem. The topic lists highlight prominent communication practices related to community building, such as AMA (ask me anything) sessions and airdrops.
Abstract As blockchain technology advances, non-fungible tokens (NFTs) are emerging as unconventional assets in the commercial market. However, it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns. This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter, Weibo, and Reddit. Employing text clustering and topic modeling techniques, such as Latent Dirichlet Allocation, we constructed an analytical framework to delve into the intricacies of the NFT ecosystem. Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data. Weibo users predominantly engaged in reviews and critiques, whereas Twitter and Reddit users emphasized personal experiences and perceptions. The NFT ecosystem encompasses several crucial elements, including transactions, customers, infrastructure, products, environments, and perceptions. By identifying the prevailing trends and common issues, this study offers valuable guidance for the development of NFT ecosystems.
The nexus of green finance and digital banking is transforming the world financial system on the twin pillars of environmental sustainability and technological innovation. Topic modeling is utilized in this study to examine nascent trends on the basis of a corpus of around 481 records of the Web of Science database. Six leading topics are: (1) Digital Financial Inclusion and Sustainable Development, (2) Green Finance and Digital Innovation, (3) Fintech and Sustainable Financial Services, (4) Climate and Environmental Sustainability Digital Banking, (5) Blockchain and Transparency in Sustainable Finance, and (6) AI and Big Data in Sustainable Financial DecisionMaking. Digital banking is enabling financial inclusion, especially in rural villages, and supporting the United National Sustainable Development Goals (SDGs). Fintech technologies such as mobile banking, blockchain, and AI are propelling access to green financial products, transparency, and climate risk analysis. Blockchain is providing traceability of green bond issuance, while AI-based tools are offering real-time analysis of sustainability risk. Fintech innovation such as ESG-driven robo-advisors are giving access to sustainable financial services to everyone and facilitating decentralized investment in clean energy projects. Yet, issues like digital literacy deficits, cyber-attacks, and the environmental cost of blockchain mining persist. Regulatory schemes must continue to change and meet these to facilitate the promotion of inclusive access to sustainable financial services. This essay points out the necessity of harmonized ESG reporting mechanisms, AI transparency in governance, and inclusive regulation for facilitating the incorporation of sustainability in electronic banking. The findings point out the transformative potential of digital technologies in remoulding sustainable finance with significant implications for financial institutions, regulators, and academics. Subsequent work must take note of developing technology like quantum computing and decentralized finance (DeFi) to continue advancing sustainable financial innovation.
Purpose This study aims to analyze public discourse on decentralized finance (DeFi) and central bank digital currencies (CBDC) using advanced natural language processing (NLP) techniques to uncover key insights that can guide financial policy and innovation. This research seeks to fill the gap in the existing literature by applying state-of-the-art NLP models like BERT and RoBERTa to understand the evolving online discourse around DeFi and CBDC. Design/methodology/approach This study uses a multilabel classification using BERT and RoBERTa models alongside BERTopic for topic modeling. Data is collected from social media platforms, including Twitter and LinkedIn, as well as relevant documents, to analyze public sentiment and discourse. Model performance is evaluated based on accuracy, precision, recall and F1-scores. Findings RoBERTa outperforms BERT in classification accuracy and precision across all metrics, making it more effective in categorizing public discourse on DeFi and CBDC. BERTopic identifies five key topics frequently discussed, such as financial inclusion, competition and growth in DeFi, with important implications for policymakers. Practical implications The insights derived from this study provide valuable information for financial regulators and policymakers to develop more informed, data-driven strategies for implementing and regulating DeFi and CBDC. Public discourse analysis enables policymakers to understand emerging concerns and trends critical for crafting effective financial policies. Originality/value This study is among the first to use advanced NLP models, including RoBERTa and BERTopic, to analyze public discourse on DeFi and CBDC. It offers novel insights into the potential challenges and opportunities these innovations present. It contributes to the growing body of research on the intersection of digital financial technologies and public sentiment.
Wenjie Qu, Yijun Sun, Xuanming Liu, Tao LU · 7 authors
Large Language Models (LLMs) are widely employed for their ability to generate human-like text. However, service providers may deploy smaller models to reduce costs, potentially deceiving users. Zero-Knowledge Proofs (ZKPs) offer a solution by allowing providers to prove LLM inference without compromising the privacy of model parameters. Existing solutions either do not support LLM architectures or suffer from significant inefficiency and tremendous overhead. To address this issue, this paper introduces several new techniques. We propose new methods to efficiently prove linear and nonlinear layers in LLMs, reducing computation overhead by orders of magnitude. To further enhance efficiency, we propose constraint fusion to reduce the overhead of proving non-linear layers and circuit squeeze to improve parallelism. We implement our efficient protocol, specifically tailored for popular LLM architectures like GPT-2, and deploy optimizations to enhance performance. Experiments show that our scheme can prove GPT-2 inference in less than 25 seconds. Compared with state-of-the-art systems such as Hao et al. (USENIX Security’24) and ZKML (Eurosys’24), our work achieves nearly 279× and 185× speedup, respectively.
Jan 1, 2025·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Andrea Peña Calvín, David Duenas-Cid, Junaid Ahmed
This study analyzes voting dynamics and proposal outcomes within Decentraland, a prominent Decentralized Autonomous Organization (DAO), by examining its voting behaviors and decision outcomes. We offer insights into how a DAO is employed to facilitate decision-making and discern the nature of the issues about which decisions are made. DAOs promise horizontal and democratic decision-making. However, our research reveals a high concentration of voting power among a few members despite them not utilizing it to a great extent. Additionally, we identify the prevailing themes in decision-making processes within the organization through topic modeling. The primary topics identified are the effective management and governance of the platform and community and the platform’s strategic growth, with a particular emphasis on wearable technology. This research addresses fundamental questions regarding the democratic integrity of DAOs and their ability to achieve equitable representation and decision-making.
This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020. We compare traditional practices-such as keyword-based algorithms and embedding techniques-with state-of-the-art topic modeling methods that employ dimensionality reduction and clustering. This comparison allows us to assess the popularity and evolution of different approaches and themes within algorithmic trading. We demonstrate the usefulness of Natural Language Processing (NLP) in the automatic extraction of knowledge, highlighting the new possibilities created by the latest iterations of Large Language Models (LLMs) like ChatGPT. The rationale for focusing on this topic stems from our analysis, which reveals that research articles on algorithmic trading are increasing at a faster rate than the overall number of publications. While stocks and main indices comprise more than half of all assets considered, certain asset classes, such as cryptocurrencies, exhibit a much stronger growth trend. Machine learning models have become the most popular methods in recent years. The study demonstrates the efficacy of LLMs in refining datasets and addressing intricate questions about the analyzed articles, such as comparing the efficiency of different models. Our research shows that by decomposing tasks into smaller components and incorporating reasoning steps, we can effectively tackle complex questions supported by case analyses. This approach contributes to a deeper understanding of algorithmic trading methodologies and underscores the potential of advanced NLP techniques in literature reviews.
As a disruptive technology, blockchain has become a strategic priority for many businesses. A vast amount of research exists on blockchain's innovative nature and immense potential for multiple industries. This study aims to synthesize the existing research to classify the findings into various themes and propose avenues for further research. A total of 2,360 academic articles were analyzed using the text-mining method of structural topic modeling. The identified fifteen topics were mapped to the four quadrants of the Datatopia model, leading to the development of the Datatopia-blockchain (DBlock) framework. The results present future scenarios that provide an understanding of what is known about blockchain, its characteristics, and potential research areas. The contributions to the theory and implications to the practitioners are discussed in detail.
The large-scale deployment of Solidity smart contracts on the Ethereum mainnet has increasingly attracted financially-motivated attackers in recent years. A few now-infamous attacks in Ethereum's history includes DAO attack in 2016 (50 million dollars lost), Parity Wallet hack in 2017 (146 million dollars locked), Beautychain's token BEC in 2018 (900 million dollars market value fell to 0), and NFT gaming blockchain breach in 2022 ($600 million in Ether stolen). This paper presents a comprehensive investigation of the use of large language models (LLMs) and their capabilities in detecting OWASP Top Ten vulnerabilities in Solidity. We introduce a novel, class-balanced, structured, and labeled dataset named VulSmart, which we use to benchmark and compare the performance of open-source LLMs such as CodeLlama, Llama2, CodeT5 and Falcon, alongside closed-source models like GPT-3.5 Turbo and GPT-4o Mini. Our proposed SmartVD framework is rigorously tested against these models through extensive automated and manual evaluations, utilizing BLEU and ROUGE metrics to assess the effectiveness of vulnerability detection in smart contracts. We also explore three distinct prompting strategies-zero-shot, few-shot, and chain-of-thought-to evaluate the multi-class classification and generative capabilities of the SmartVD framework. Our findings reveal that SmartVD outperforms its open-source counterparts and even exceeds the performance of closed-source base models like GPT-3.5 and GPT-4 Mini. After fine-tuning, the closed-source models, GPT-3.5 Turbo and GPT-4o Mini, achieved remarkable performance with 99% accuracy in detecting vulnerabilities, 94% in identifying their types, and 98% in determining severity. Notably, SmartVD performs best with the `chain-of-thought' prompting technique, whereas the fine-tuned closed-source models excel with the `zero-shot' prompting approach.
Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally.
Purpose The trend among the financial investors to integrate cryptocurrencies, the very first completely digital assets, in their investment portfolio, has increased during the last decade. Even though cryptocurrencies share certain common characteristics with other investment products, they have their own distinct characteristic features, and the behavior of this asset class is currently being studied by the research scholars interested in this domain. Design/methodology/approach Using the text mining approach, this article examines research trends in the field of cryptocurrencies to identify prospective research needs. To narrow down to ten topics, the abstracts and the indexed keywords of 1,387 research publications on cryptocurrency, blockchain and Bitcoins published between 2013 and 2022 were analyzed using the topic modeling technique and Latent Dirichlet allocation (LDA). Findings The findings show a wide range of study trends on various aspects of cryptocurrencies. In the recent years, there have been lots of research and publications on the topics such as cryptocurrency markets, cryptocurrency transactions and use of blockchain in transactions and security of Bitcoin. In comparison, topics such as use of blockchain in fintech, cryptocurrency regulations, blockchain smart contract protocols and legal issues in cryptocurrency have remained relatively underexplored. After using the LDA, this paper further analyzes the significance of each topic, future directions of individual topics and its popularity among researchers in the discussion section. Originality/value While similar studies exist, no other work has used topic modeling to comprehensively analyze the cryptocurrencies literature by considering diverse fields and domains.
This research introduces a cutting-edge Web3 literary analysis platform, harnessing the power of blockchain and deep learning technologies. By employing the immutable and transparent nature of blockchain, the platform ensures robust copyright protection while offering readers enhanced interactive features. It applies deep learning techniques for comprehensive analyses of sentiment, topic, and stylistic elements, which are instrumental in predicting potential Nobel Prize laureates. This methodology not only enhances the accuracy of predictions but also sheds light on the evaluation criteria and historical trends associated with the Nobel Prize. Moreover, the platform adopts a directed graph model alongside the struc2vec algorithm to create text vectors for comparative studies, uncovering similarities between works that have won awards and those that have been nominated. Utilizing the LESS model for detailed content examination, the platform delves into sequence relationships within semantic networks, thus improving interpretability and visualization. The integration of blockchain technology guarantees access to unbiased datasets, enabling more precise literary analyses and predictions. This innovative approach has been validated using works that have either won or been nominated for the Nobel Prize, proving its efficacy in identifying the textual characteristics favored by the Nobel Prize committee.
Caleb Geren, Amanda Board, Gaby G. Dagher, Tim Andersen · 5 authors
With the growing development and deployment of large language models (LLMs) in both industrial and academic fields, their security and safety concerns have become increasingly critical. However, recent studies indicate that LLMs face numerous vulnerabilities, including data poisoning, prompt injections, and unauthorized data exposure, which conventional methods have struggled to address fully. In parallel, blockchain technology, known for its data immutability and decentralized structure, offers a promising foundation for safeguarding LLMs. In this survey, we aim to comprehensively assess how to leverage blockchain technology to enhance LLMs' security and safety. Besides, we propose a new taxonomy of blockchain for large language models (BC4LLMs) to systematically categorize related works in this emerging field. Our analysis includes novel frameworks and definitions to delineate security and safety in the context of BC4LLMs, highlighting potential research directions and challenges at this intersection. Through this study, we aim to stimulate targeted advancements in blockchain-integrated LLM security.
This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.
Recently, with the prevalent adoption of blockchain in the financial system, there has been an increasing of anomaly activities such as ponzi schemes, gambling and phishing fraud on Ethereum platforms, and an effective account classification method is urgently required. The existing account classification methods on Ethereum with high accuracy require a learning system to be trained with balanced datasets. However, the distribution of annotated labels for account identities published on third-party sites is relatively imbalanced. Therefore, in this paper, We propose a EthGAN framework which includes a high-dimensional node feature representation module and a few-shot account data augment module to improve the accuracy and robustness at imbalanced datasets. The high-dimensional node feature representation module captures features from statistical, temporal, and transaction structure, and the few-shot account data augmentation module based on generative adversarial network models generate few-shot samples to improve the diversity and representativeness of the training datasets. We conduct extensive experiments to evaluate the performance of our proposed EthGAN framework on real-world Ethereum transaction data. The average classification effect of our method is 10+% higher than that of existing methods. Experimental results demonstrate that our method outperforms state-of-the-art methods in Ethereum account classification.
This study aims to explore social media content associated with cryptocurrency marketing. We employ unsupervised Latent Dirichlet allocation topic modeling and sentiment analysis techniques to 98,716 tweets to examine Twitter (now known as X) content for subjects and sentiments related to cryptocurrency. Our findings reveal that cryptocurrency tweets fell into four categories, with ‘cryptocurrency trading,’ ‘NFT airdrop,’ ‘cryptocurrency affiliate program,’ and ‘Dogecoin on social media’ being the most popular. Furthermore, most of these topics exhibited positive sentiments. This study contributes theoretically by integrating cryptocurrency marketing into the diffusion of innovation paradigm. In addition, it offers strategic insights for digital marketers in identifying prevalent topics and sentiments related to cryptocurrency, enabling the tailoring of affiliate marketing communication strategies on social media.
Blockchain Technology Applications and Security
Consumer Behavior in Brand Consumption and Identification
Recent advances in the field of large language models (LLMs), particularly the ChatGPT family, have given rise to a powerful and versatile machine interlocutor, packed with knowledge and challenging our understanding of learning. This interlocutor is a double-edged sword: it can be harnessed for a wide variety of beneficial tasks, but it can also be used to cause harm. This study explores the complicated interaction between ChatGPT and the growing problem of cryptocurrency fraud. Although ChatGPT is known for its adaptability and ethical considerations when used for harmful purposes, we highlight the deep connection that may exist between ChatGPT and fraudulent actions in the volatile cryptocurrency ecosystem. Based on our categorization of cryptocurrency frauds, we show how to influence outputs, bypass ethical terms, and achieve specific fraud goals by manipulating ChatGPT prompts. Furthermore, our findings emphasize the importance of realizing that ChatGPT could be a valuable instructor even for novice fraudsters, as well as understanding and safely deploying complex language models, particularly in the context of cryptocurrency frauds. Finally, our study underlines the importance of using LLMs responsibly and ethically in the digital currency sector, identifying potential risks and resolving ethical issues. It should be noted that our work is not intended to encourage and promote fraud, but rather to raise awareness of the risks of fraud associated with the use of ChatGPT.
Xintong Wu, Wanling Deng, Yutong Quan, Lin William Cong · 5 authors
Trust mechanisms diverge between centralized and decentralized exchanges, representing distinct sociotechnical governance paradigms. However, quantifying trust dynamics and their redistribution between these architectures remains empirically challenging, limiting understanding of how institutional shocks affect market behavior. The FTX collapse offers a natural experiment to bridge this gap. Through an interdisciplinary approach combining causal inference and computational text analysis, we find significant price declines and capital reallocation from centralized to decentralized exchanges following the event. While sentiment metrics showed no sharp discontinuities, topic modeling and network analysis of Discord communities reveal that seasonal holiday discourse obscured underlying trust concerns in centralized exchange forums. These findings underscore the fragility of institutional trust architectures and demonstrate how mixed methods can illuminate behavioral patterns during systemic crises, offering insights for exchange risk management and regulatory assessment.
Haochen Sun, J. Li, Change Institutions to: University of Waterloo
The recent surge in artificial intelligence (AI), characterized by the prominence of large language models (LLMs), has ushered in fundamental transformations across the globe. However, alongside these advancements, concerns surrounding the legitimacy of LLMs have grown, posing legal challenges to their extensive applications. Compounding these concerns, the parameters of LLMs are often treated as intellectual property, restricting direct investigations. In this study, we address a fundamental challenge within the realm of AI legislation: the need to establish the authenticity of outputs generated by LLMs. To tackle this issue, we present zkLLM, which stands as the inaugural specialized zero-knowledge proof tailored for LLMs to the best of our knowledge. Addressing the persistent challenge of non-arithmetic operations in deep learning, we introduce tlookup, a parallelized lookup argument designed for non-arithmetic tensor operations in deep learning, offering a solution with no asymptotic overhead. Furthermore, leveraging the foundation of tlookup, we introduce zkAttn, a specialized zero-knowledge proof crafted for the attention mechanism, carefully balancing considerations of running time, memory usage, and accuracy. Empowered by our fully parallelized CUDA implementation, zkLLM emerges as a significant stride towards achieving efficient zero-knowledge verifiable computations over LLMs. Remarkably, for LLMs boasting 13 billion parameters, our approach enables the generation of a correctness proof for the entire inference process in under 15 minutes. The resulting proof, compactly sized at less than 200 kB, is designed to uphold the privacy of the model parameters, ensuring no inadvertent information leakage.
We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanism that decomposes a complex question into a reasoning chain via systematic prompting and pre-designed actions. Methodologically, we propose three types of domain-adaptable `Plug-and-Play' actions for retrieving real-time information from heterogeneous sources. We also propose a multi-reference faith score (MRFS) to verify and resolve conflicts in the answers. Empirically, we exploit both public benchmarks and a Web3 case study to demonstrate the capability of CoA over other methods.
Abstract This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.
Smart contract code summarization is crucial for efficient maintenance and vulnerability mitigation. While many studies use Large Language Models (LLMs) for summarization, their performance still falls short compared to fine-tuned models like CodeT5+ and CodeBERT. Some approaches combine LLMs with data flow analysis but fail to fully capture the hierarchy and control structures of the code, leading to information loss and degraded summarization quality. We propose SCLA, an LLM-based method that enhances summarization by integrating a Control Flow Graph (CFG) and semantic facts from the code's control flow into a semantically enriched prompt. SCLA uses a control flow extraction algorithm to derive control flows from semantic nodes in the Abstract Syntax Tree (AST) and constructs the corresponding CFG. Code semantic facts refer to both explicit and implicit information within the AST that is relevant to smart contracts. This method enables LLMs to better capture the structural and contextual dependencies of the code. We validate the effectiveness of SCLA through comprehensive experiments on a dataset of 40,000 real-world smart contracts. The experiment shows that SCLA significantly improves summarization quality, outperforming the SOTA baselines with improvements of 26.7%, 23.2%, 16.7%, and 14.7% in BLEU-4, METEOR, ROUGE-L, and BLEURT scores, respectively.