Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies' existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months.
Due to the decentralized and public nature of the blockchain ecosystem, malicious activities on the Ethereum platform impose immeasurable losses on users. At the same time, the transparency of cryptocurrency transactions provides a unique opportunity to analyze illegal activities, such as phishing scams, from a network perspective. Most existing phishing scam detection methods focus primarily on analyzing account interaction networks, which limits their ability to uncover transaction behavior patterns embedded within transaction interactions. To address this, we construct theTransactionSubGraphNetwork (TSGN) by using transaction subgraphs as basic elements and further propose a novel framework for Ethereum phishing account detection. Specifically, we rebuild the graph structures via three well-designed mapping mechanisms, yielding TSGN and its two variants, i.e., Directed-TSGN and Temporal-TSGN, to obtain direction-aware and time-aware transfer flow features. By further incorporating the mapping strategy into transaction multidigraphs, we develop the Multiple-TSGN, which could preserve more transaction flow features while concurrently reducing the time consumption of modeling large-scale networks. TSGN models based on transaction subgraph interactions can capture complex higher-order dependencies, which lay beyond the reach of models that exclusively capture pairwise account interactions. As a general framework, our model can incorporate various feature extraction methods to improve the performance of phishing detection. Extensive experimental results on Ethereum datasets show that our method achieves superior performance in phishing detection, yielding 3.27%$\sim$6.71% relative improvement over previous state-of-the-art.
Blockchain, the technology infrastructure behind the famous cryptocurrency bitcoin, can take away the notion of trust from centralized organizations to a decentralized platform that is mathematically verifiable and cryptographically secure. It is gaining more significant momentum exponentially and disrupts the way businesses function beyond the digital currency aspects. This work presents a text mining literature analysis of research articles published in major digital libraries on blockchain technology and cybersecurity. This literature analysis employs automated text mining approaches such as topic modeling and keyphrase extraction for unearthing the themes from a vast body of literature. This analysis highlights the multidisciplinary nature of blockchain technology within the cybersecurity domain. The findings also show the cyber threats and vulnerabilities that evolve with blockchain technology developments. This analysis also showcases the computer security research community’s vulnerabilities and provides future research dimensions that are crucial for designing secure blockchain applications and platforms.
Bu çalışma, algı yönetiminin Bitcoin fiyatlarına etkisini göstermek amacıyla gerçekleştirilmiştir. Algı yönetimi, çeşitli stratejiler uygulanarak hedeflenen düşüncelerin kitlelere kabul ettirilmesi şeklinde ifade edilmektedir. Küreselleşme ve dijitalleşmenin etkisiyle algı yönetiminin siyasetçiler, gazeteciler ve bireyler tarafından sıklıkla kullanıldığı gözlemlenmektedir. Algı yönetiminin günümüzde dijital medya olanaklarıyla gerçekleştirildiği ve düşüncelerin çeşitli platformlar aracılığı ile ifade edildiği görülmektedir. Kimi mecralarda manipülasyona neden olan algı yönetimi, genellikle kitleler tarafından izlenen kişilerce gerçekleştirilmektedir. Çalışmada, Elon Musk tarafından Twitter platformu üzerinden gerçekleştirilen paylaşımların, Bitcoin fiyatlarına etkisi gösterilmiştir. Çalışmada Musk tarafından gerçekleştirilen üç paylaşım ile o tarihlerde Bitcoin fiyat hareketliliği incelenmiştir. Çalışma sonucunda Musk tarafından gerçekleştirilen paylaşımlar ile Bitcoin fiyatları üzerinde gerçekleştirilen olumlu ve olumsuz algı yönetiminin doğrusal olduğu bulgusu elde edilmiştir. Özellikle belirtmek gerekir ki Bitcoin fiyatının düşüş ya da yükseliş hareketinde tek etkili olan faktör Musk’ın paylaşımları olmamakla beraber, bu paylaşımlar fiyatlara büyük oranda etki etmiştir. Daha sonra gerçekleştirilen çalışmalarda farklı kişilerce gerçekleştirilen algı yönetimi örneklerinin analiz edilmesi önerilmektedir.
M.H. Zafar, Iliyas Karim Khan, Anees ur Rehman, Sadia Zafar
In Pakistan, existing blood control systems or blood information management systems are limited in terms of efficient data retrieval of donor to consumer. There is no communication network in place for extra blood in one location to be demanded from a region if blood is limited, resulting in blood wastage. Due to a lack of accessibility and sufficient blood quality testing, blood contaminated with illnesses such as HIV has been used for transfusion in some cases. This study proposes a ledger blood management system to address these challenges. The trail has been represented as a supply-chain management problem following the blood. By trailing the blood stream and donation a single platform for transferring blood and the problem results among blood groups, the proposed system, built on the hyperledger fabric model, adds more traceability toward the blood transfusion process. It also helps to reduce unjustified blood wastage by providing an integrated system for transferring lifeblood and the thing extracts among lifeblood banks. A web app is also designed for accessing the network for simplicity of usage and security is enhanced by implementing block chain hyperfebric ledger system through Key Value System (KVS) system.
Cross-Chain bridges have become the most popular solution to support asset interoperability between heterogeneous blockchains. However, while providing efficient and flexible cross-chain asset transfer, the complex workflow involving both on-chain smart contracts and off-chain programs causes emerging security issues. In the past year, there have been more than ten severe attacks against cross-chain bridges, causing billions of loss. With few studies focusing on the security of cross-chain bridges, the community still lacks the knowledge and tools to mitigate this significant threat. To bridge the gap, we conduct the first study on the security of cross-chain bridges. We document three new classes of security bugs and propose a set of security properties and patterns to characterize them. Based on those patterns, we design Xscope, an automatic tool to find security violations in cross-chain bridges and detect real-world attacks. We evaluate Xscope on four popular cross-chain bridges. It successfully detects all known attacks and finds suspicious attacks unreported before. A video of Xscope is available at https://youtu.be/vMRO_qOqtXY.
Proof-of-Work (PoW) is a Sybil control mechanism adopted in blockchain-based cryptocurrencies. It prevents the attempt of malicious actors to manipulate distributed ledgers. Bitcoin has successfully suppressed double-spending by accepting the longest PoW chain. Nevertheless, PoW encountered several major security issues surrounding mining competition. One of them is a Block WithHolding (BWH) attack that can exploit a widespread and cooperative environment called a mining pool. This attack takes advantage of untrustworthy relationships between mining pools and participating agents. Moreover, detecting or responding to attacks is challenging due to the nature of mining pools. In this paper, however, we suggest that BWH attacks also have a comparable trust problem. Because a BWH attacker cannot have complete control over BWH agents, they can betray the belonging mining pool and seek further benefits by trading with victims. We prove that this betrayal is not only valid in all attack parameters but also provides double benefits; finally, it is the best strategy for BWH agents. Furthermore, our study implies that BWH attacks may encounter self-destruction of their own revenue, contrary to their intention.
Along with the advancements in blockchain technology, many blockchain-based successful projects have been done mainly on the ethereum platform, most of which deal with transactions.Still, it also carries various risks when it comes to security, as evident from past attacks.Most big projects like uniswap, decentraland, and others use smart contracts, deployed on the ethereum platform, leading to similar projects via code reuse.Code reuse practice is quite frequent as a survey suggests 26% of contract code deployed is via code reuse.Smart contract code obfuscation techniques can be used on solidity code that is publicly verified, published (in the case of Ethereum), and on the deployment address.All the above techniques work by replacing characters with their random counterpart, known as statistical substitution.A statistical substitution is a process of transforming an input string into a new string where each character has been replaced by a random character drawn from a stock of all possible 'random' characters.Therefore, we proposed numerous methods in this paper to solve the above problems using various smart contract code obfuscation techniques.These techniques can be really useful in blockchain projects and can save millions of dollars to investors & companies by enhancing code security and preventing code reusability.Techniques mentioned in this paper when compared with other techniques.Our methods are not expensive to implement, very easy to use, and provide a developer-friendly selective increment in code complexity.
Recently, Decentralized Finance (DeFi) platforms on Ethereum are booming, and numerous traders are trying to capitalize on the opportunity for maximizing their benefits by launching front-running attacks and extracting Miner Extractable Values (MEVs) based on information in the public mempool. To protect end users from being harmed and hide transactions from the mempool, private transactions, a special type of transactions that are sent directly to miners, were invented. Private transactions have a high probability of being packed to the front positions of a block and being added to the blockchain by the target miner, without going through the public mempool, thus reducing the risk of being attacked by malicious entities. Despite the good intention of inventing private transactions, due to their stealthy nature, private transactions have also been used by attackers to launch attacks, which has a negative impact on the Ethereum ecosystem. However, existing works only touch upon private transactions as by-products when studying MEV, while a systematic study on private transactions is still missing. To fill this gap and paint a complete picture of private transactions, we take the first step towards investigating the private transactions on Ethereum. In particular, we collect large-scale private transaction datasets and perform analysis on their characteristics, transaction costs and miner profits, as well as security impacts. This work provides deep insights on different aspects of private transactions.
Blockchain is making a big impact in various applications, but it is also attracting a variety of cybercrimes. In blockchain, phishing transfers the victim’s virtual currency to make huge profits through fraud, which poses a threat to the blockchain ecosystem. To avoid greater losses, Ethereum, one of the blockchain platforms, can provide information to detect phishing fraud. In this study, to effectively detect phishing nodes, we propose a phishing node detection approach as message passing based graph convolution network. We first form a transaction network through the transaction records of Ethereum and then extract the information of nodes effectively via message passing. Finally, we use a graph convolution network to classify the normal and phishing nodes. Experiments show that our method is effective and superior to other existing methods.
Matheus V. X. Ferreira, Ye Lin Sally Hahn, S. Matthew Weinberg, Catherine Yu
Cryptographic Self-Selection is a subroutine used to select a leader for modern proof-of-stake consensus protocols, such as Algorand. In cryptographic self-selection, each round $r$ has a seed $Q_r$. In round $r$, each account owner is asked to digitally sign $Q_r$, hash their digital signature to produce a credential, and then broadcast this credential to the entire network. A publicly-known function scores each credential in a manner so that the distribution of the lowest scoring credential is identical to the distribution of stake owned by each account. The user who broadcasts the lowest-scoring credential is the leader for round $r$, and their credential becomes the seed $Q_{r+1}$. Such protocols leave open the possibility of a selfish-mining style attack: a user who owns multiple accounts that each produce low-scoring credentials in round $r$ can selectively choose which ones to broadcast in order to influence the seed for round $r+1$. Indeed, the user can pre-compute their credentials for round $r+1$ for each potential seed, and broadcast only the credential (among those with a low enough score to be the leader) that produces the most favorable seed. We consider an adversary who wishes to maximize the expected fraction of rounds in which an account they own is the leader. We show such an adversary always benefits from deviating from the intended protocol, regardless of the fraction of the stake controlled. We characterize the optimal strategy; first by proving the existence of optimal positive recurrent strategies whenever the adversary owns last than $38\%$ of the stake. Then, we provide a Markov Decision Process formulation to compute the optimal strategy.
Traceability and trustiness are two critical issues in the logistics sector. Blockchain provides a potential way for logistics tracking systems due to its traits of tamper resistance. However, it is non-trivial to apply blockchain on logistics because of firstly, the binding relationship between virtue data and physical location cannot be guaranteed so that frauds may exist. Secondly, it is neither practical to upload complete data on the blockchain due to the limited storage resources nor convincing to trust the digest of the data. This paper proposes a traceable and trustable consortium blockchain for logistics T2L to provide an efficient solution to the mentioned problems. Specifically, the authenticated geocoding data from telecom operators’ base stations are adopted to ensure the location credibility of the data before being uploaded to the blockchain for the purpose of reliable traceability of the logistics. Moreover, we propose a scheme based on Zero Knowledge Proof of Retrievability (ZK BLS-PoR) to ensure the trustiness of the data digest and the proofs to the blockchain. Any user in the system can check the data completeness by verifying the proofs instead of downloading and examining the whole data based on the proposed ZK BLS- PoR scheme, which can provide solid theoretical verification. In all, the proposed T2L framework is a traceable and trustable logistics system with a high level of security.
It has long been difficult to create a safe electronic voting system that provides the transparency and flexibility provided by electronic systems, while maintaining the fairness and privacy of present voting methods. Voting, especially during elections, is a technique where participants do not trust one another since the system might be attacked not just by an outsider but also by participants themselves (voters and organizers). The traditional methods of voting systems find it challenging to maintain the characteristics of an ideal voting system since there is a chance of tampering with results and disturbing the process itself. As a result, the effectiveness of the voting system is increased by translating the characteristics of an ideal voting system into digital space. It greatly lowers the expense of the elections and the work of the inspectors. In this essay, we'll use the open-source Blockchain technology to suggest a new electronic voting system's architecture. New chances to create new kinds of digital services are being provided by Blockchain. Numerous elements of our life have been altered by Blockchain technology, including the ability to save digital transactions via the Internet, confirm their legitimacy, license them, and provide the greatest level of security and encryption. This system offers a distributed architecture for storing the data, which distributes the data among many servers. In addition to maintaining voter identity outside of the vote count, this technology makes the voting process transparent.
Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner's credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner's accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila's predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners' credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.
Amer A. Sallam, Taha H. Rassem, Hanadi Abdu, Haneen Abdulkareem · 6 authors
On the Ethereum network, users communicate with one another through a variety of different accounts. Pseudo-anonymity was enforced over the network to provide the highest level of privacy. By using accounts that engage in fraudulent activity across the network, such privacy may be exploited. Like other cryptocurrencies, Ethereum blockchain may exploited with several fraudulent activities such as Ponzi schemes, phishing, or Initial Coin Offering (ICO) exits, etc. However, the identification of parameters with abnormal account characteristics is not an easy task and requires an intelligent approach to distinguish between normal and fraudulent activities. Therefore, this paper has attempted to solve this a problem by using machine learning techniques to introduce a robust approach that can detect fraudulent accounts on Ethereum. We have used a K-Nearest Neighbor, Random Forest and XGBoost over a collected dataset of 4,681 instances along with 2,179 fraudulent accounts associated and 2,502 regular accounts. The XGBoost, RF, and KNN techniques achieved average accuracies of 96.80 %, 94.8 8%, and 87.85% and an average AUC of 0.995, 0.99 and 0.93, respectively.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Jinho Choi, Jaehan KIM, Minkyoo Song, Hanna KIM · 8 authors
Cryptocurrency abuse has become a critical problem. Due to the anonymous nature of cryptocurrency, criminals commonly adopt cryptocurrency for trading drugs and deceiving people without revealing their identities. Despite its significance and severity, only few works have studied how cryptocurrency has been abused in the real world, and they only provide some limited measurement results. Thus, to provide a more in-depth understanding on the cryptocurrency abuse cases, we present a large-scale analysis on various Bitcoin abuse types using 200,507 real-world reports collected by victims from 214 countries. We scrutinize observable abuse trends, which are closely related to real-world incidents, to understand the causality of the abuses. Furthermore, we investigate the semantics of various cryptocurrency abuse types to show that several abuse types overlap in meaning and to provide valuable insight into the public dataset. In addition, we delve into abuse channels to identify which widely-known platforms can be maliciously deployed by abusers following the COVID-19 pandemic outbreak. Consequently, we demonstrate the polarization property of Bitcoin addresses practically utilized on transactions, and confirm the possible usage of public report data for providing clues to track cyber threats. We expect that this research on Bitcoin abuse can empirically reach victims more effectively than cybercrime, which is subject to professional investigation.
With the evolution of technology, blockchain a swiftly impending phenomenon i.e., "decentralized computing” is observed. The emergence of Smart Contracts (SC) has resulted in advancements in the application of blockchain technology. The Ethereum network’s computing capabilities and functionalities are founded on the basis of SC. A smart contract is a self-executing agreement between buyer and seller with the terms of the settlement between them, written directly as lines of code, existing across a distributed decentralized blockchain network. It is a decentralized software that runs on a blockchain autonomously, consistently, and publicly. Conversely, due to the complex semantics of fundamental domain-specific languages and their testability, constructing reliable and secure SC can be extremely difficult. SC might contain some vulnerabilities. Security vulnerabilities can originate from financial tribulations; there are a number of notorious events that specify blockchain SC could comprise numerous code-security vulnerabilities. Security and privacy of blockchain-based SC are very important, we must first identify their vulnerabilities before implementing them widely. Therefore, the purpose of this paper is to conduct a comprehensive experimental evaluation of two current security testing tools: Remix solidity static analysis plugin and Solium which are used for static analysis of SC. We have conducted an empirical analysis of SC for finding tangible and factual evidence, controlled by the scientific approach. The methodology’s first step is to gather all of the Ethereum SC and store them in a repository. The next step is to use the Remix solidity static analysis plugin and Solium to perform vulnerability assessments. The last step is to analyze the result of both tools and evaluate them on the basis of accuracy and effectiveness. The goal of this empirical analysis is to evaluate the two FOSS tools: Remix solidity static analysis plugin and Solium on the basis of accuracy and effectiveness. Some research questions were considered to reach the stated goal: What automated tools and frameworks are proposed in supporting the state-of-the-art empirical approach to SC vulnerability detection? How accurate are security analysis tools? And which tool has more accuracy rate? How effectively security analysis tools are detecting vulnerabilities in SC? And which is the most effective security analysis tool? We investigated the effectiveness and accuracy of security code analysis tools on Ethereum by testing them on a random sample of vulnerable contracts. The results indicate that the tools have significant discrepancies when it comes to certain security characteristics. In terms of effectiveness and accuracy, the Remix plugin outperformed and beat the other tool.
Social media has transformed the mode of communication globally by providing an extensive system for exchanging ideas, initiating business contracts, and proposing new professional ideas. However, there are many limitations to the use of social media, such as misinformation, lack of effective content moderation, digital piracy, data breaches, identity fraud, and fake news. In order to address these limitations, several studies have introduced the application of Blockchain technology in social media. Blockchains can provides transparency, traceability, tamper-proofing, confidentiality, security, information control, and supervision. This paper is a systematic literature review of papers covering the application of Blockchain technology in social media. To the best of our knowledge, this is the first systematic literature review that elucidates the combination of Blockchain and social media. Using several electronic databases, 42 related papers were reviewed. Our findings show that previous studies on the applications of Blockchain in social media are focused mainly on blocking fake news and enhancing data privacy. Research in this domain began in 2017. This review additionally discusses several challenges in applying Blockchain technologies in social media contexts, and proposes alternative ideas for future implementation and research.
Technological advancements in block chain (BC)-based frameworks have empowered scientists to create innovative inventions such as e-casting ballots. The traditional agreement models utilized proof-of-work (PoW) in the Bitcoin that affected energy utilization and bargained the adaptability for the ballot framework. The existing works evaluates the trust basically only on the centralized party as it was not feasible because of the dynamic changes in the pervasive social networking (PSN) topology and their characteristics. The present research work proposes a block chain based trust evaluation model for the PSN based BC. The proposed hybrid proof of stake-trust (PST) BC is based on the proof-of trust (PoT) and also the proof of stake (PoS) overcomes the issues that are occurring in the e-vote casting. The trust evaluation is performed for public verification and becomes transparent for each node of PSN. The advantage of the proposed method is that a new block will be designed for trust evaluation during the block generation. The process of sharding erases the workload when the network works fast for the individual nodes provides the sum of their alternative parts. Therefore, the model utilizes the agreements for generating the safe process that guarantee the precision to vote it from the time of the election results. The present research work utilizes the proof of stake-trust based BC resulted in security improvement. The model improves the adaptability and execution of the BC based on the ballot framework provided a secured voting system for the government. The proposed PST-BC model showed better results in terms of latency as 15/s when compared with the existing models merkle hash tree -bloom filter that obtained 107.3/s and performance constraints based electron of 18/s.
Federico Cernera, Massimo La Morgia, Alessandro Mei, Francesco Sassi
In this work, we perform a longitudinal analysis of the BNB Smart Chain and Ethereum blockchain from their inception to March 2022. We study the ecosystem of the tokens and liquidity pools, highlighting analogies and differences between the two blockchains. We discover that about 60% of tokens are active for less than one day. Moreover, we find that 1% of addresses create an anomalous number of tokens (between 20% and 25%). We discover that these tokens are used as disposable tokens to perform a particular type of rug pull, which we call 1-day rug pull. We quantify the presence of this operation on both blockchains discovering its prevalence on the BNB Smart Chain. We estimate that 1-day rug pulls generated $240 million in profits. Finally, we present sniper bots, a new kind of trader bot involved in these activities, and we detect their presence and quantify their activity in the rug pull operations.
Individual mental feelings and reactions are getting more significant as they help researchers, domain experts, businesses, companies, and other individuals understand the overall response of every individual in specific situations or circumstances. Every pure and compound sentiment can be classified using a dataset, which can be in the form of Twitter text by various Twitter users. Twitter is one of the vital platforms for individuals to participate and share their ideas about different topics; it is also considered to be one of the most famous and the biggest website for micro-blogging on the Internet. One of the key purposes of this study is to classify pure and compound sentiments based on text related to cryptocurrencies, an innovative way of trading and flourishing daily. The cryptocurrency market incurs many fluctuations in the coins’ value. A small positive or negative piece of news can sensate the whole scenario about the specific cryptocurrencies. In this paper, individuals’ pure and compound sentiments based on cryptocurrency-related Twitter text are classified. The dataset is collected through the Twitter API. In WEKA, the two deployment schemes are compared; firstly, straight with single feature selection technique (Tweet to lexicon feature vector), and secondly, a tetrad of feature selection techniques (Tweet to lexicon feature vector, Tweet to input lexicon feature vector, Tweet to SentiStrength feature vector, and Tweet to embedding feature vector) are used to purify the data LibLINEAR (LL) classifier, which contains fast algorithms for linear classification using L2-regularization L2-loss support vector machines (Dual SVM). The LL classifier differs in that it can potentially alleviate the sum of the absolute values of errors rather than the sum of the squared errors and is typically much speedier. Based on the overall performance parameters, the deployment scheme containing the tetrad of feature selection techniques with the LL classifier is considered the best choice for the purpose of classification. Among machine learning techniques, LL produces effective results and gives an efficient performance compared to other prevailing techniques. The findings of this research would be beneficial for Twitter users as well as cryptocurrency traders.
Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a "Proof of Learning" consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
Ellie Rennie, Michael Zargham, Joshua Tan, Luke E. Miller · 7 authors
Blockchain governance occurs through a combination of social and technical activities, involving smart contracts, deliberation within a group, and voting. These processes are significant as they demonstrate how governance of distributed infrastructures is evolving. While typologies of blockchain governance can be constructed by gathering on-chain interactions and formal rules, other aspects are more difficult to observe, including governance interactions occurring inside discussion forums. In this article, we discuss a participatory digital ethnography technique, whereby participants and researchers use a bespoke bot to identify governance interactions occurring within project forums (on Discord). The technique is designed to be used in conjunction with the analysis of software for the purpose of mapping and understanding the “governance surface” of different protocols. We describe our tools and methods for understanding automated futures through a case study of the SourceCred community, an organization using, developing, and maintaining open source software called SourceCred. The SourceCred codebase is also used by other decentralized communities for various organizational functions, including reputation and compensation.