The dark web has become notorious for its association with illicit activities and there is a growing need for systems to automate the monitoring of this space. This paper proposes an end-to-end scalable architecture for the early identification of new Tor sites and the daily analysis of their content. The solution is built using an Open Source Big Data stack for data serving with Kubernetes, Kafka, Kubeflow, and MinIO, continuously discovering onion addresses in different sources (threat intelligence, code repositories, web-Tor gateways, and Tor repositories), downloading the HTML from Tor and deduplicating the content using MinHash LSH, and categorizing with the BERTopic modeling (SBERT embedding, UMAP dimensionality reduction, HDBSCAN document clustering and c-TF-IDF topic keywords). In 93 days, the system identified 80,049 onion services and characterized 90% of them, addressing the challenge of Tor volatility. A disproportionate amount of repeated content is found, with only 6.1% unique sites. From the HTML files of the dark sites, 31 different low-topics are extracted, manually labeled, and grouped into 11 high-level topics. The five most popular included sexual and violent content, repositories, search engines, carding, cryptocurrencies, and marketplaces. During the experiments, we identified 14 sites with 13,946 clones that shared a suspiciously similar mirroring rate per day, suggesting an extensive common phishing network. Among the related works, this study is the most representative characterization of onion services based on topics to date.
Christian Ziegler, Marcos Luiz Cavalcanti de Miranda, Guangye Cao, Gustav Arentoft · 5 authors
Our study demonstrates the effective use of Large Language Models (LLMs) for automating the classification of complex datasets. We specifically target proposals of Decentralized Autonomous Organizations (DAOs), as the clas-sification of this data requires the understanding of context and, therefore, depends on human expertise, leading to high costs associated with the task. The study applies an iterative approach to specify categories and further re-fine them and the prompt in each iteration, which led to an accuracy rate of 95% in classifying a set of 100 proposals. With this, we demonstrate the po-tential of LLMs to automate data labeling tasks that depend on textual con-text effectively.
Research on the metaverse has experienced significant growth in recent years, driven by advancements in technology, the thriving gaming industry, the expansion of social media and virtual communities, economic prospects, and the captivating vision of a new digital interface that transcends the current internet environment. To uncover research themes within the metaverse, we conducted a comprehensive analysis of research trends in this field using publications from Scopus, a widely recognized and extensively utilized scholarly database. Employing BERTopic, an advanced topic modeling technique, we analyzed 2,181 research articles focused on the metaverse. The exploration of the metaverse had humble beginnings with a single publication in 1995 but saw a substantial increase after 2020, reaching explosive growth with 1,041 publications in 2022. The application of the BERTopic model revealed 12 primary topics, each associated with significant keywords. These main topics encompass education, healthcare, blockchain, conferences, fashion, NFTs, cybersecurity, web3, research, video streaming, tinyML, and industry. Notably, among these subjects, education, healthcare, and blockchain exhibit significant research activity. In light of the global concern over the digital divide, we conducted investigations focusing on case studies involving digitally disadvantaged groups, such as individuals with visual impairments and the elderly. However, it is noteworthy that we identified only five studies addressing this issue, indicating limited research presence in this crucial area.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
The present research utilizes Topic Modelling as a methodology to acquire a deeper understanding of the goals and operations of Decentralised Autonomous Organisations (DAOs). This is achieved by examining textual data derived from the proposals put forth by these organizations. The issue at hand pertains to comprehending the multitude of ideas inside Decentralised Autonomous Organisations (DAOs) and their alignment with the respective objectives of these entities. Through the application of Topic Modelling, we aim to investigate textual patterns, identify topics, and discern significant themes within the decentralized autonomous organization (DAO) ecosystem. This research endeavor seeks to address the existing research gaps pertaining to the alignment of proposals with organizational objectives. This research aims to fill these knowledge gaps by examining the unique thematic priorities of various decentralized autonomous organizations (DAOs), providing insights into their functions, and elucidating their involvement in investment, community, technological, and monetary issues. Through the utilization of experimental research, this research provides DAO stakeholders with the ability to make wellinformed judgements, prioritize ideas, and customize methods in order to more effectively match with their distinct missions and objectives. Consequently, this research contributes to the enhancement of operational efficiency and governance within DAOs.
The previous smart contract code comment (SCC) generation approaches can be divided into two categories: fine-tuning paradigm-based approaches and information retrieval-based approaches. However, for the fine-tuning paradigm-based approaches, the performance may be limited by the quality of the gathered dataset for the downstream task and they may have knowledge-forgetting issues. While for the information retrieval-based approaches, it is difficult for them to generate high-quality comments if similar code does not exist in the historical repository. Therefore we want to utilize the domain knowledge related to SCC generation in large language models (LLMs) to alleviate the disadvantages of these two types of approaches. In this study, we propose an approach SCCLLM based on LLMs and in-context learning. Specifically, in the demonstration selection phase, SCCLLM retrieves the top-k code snippets from the historical corpus by considering syntax, semantics, and lexical information. In the in-context learning phase, SCCLLM utilizes the retrieved code snippets as demonstrations, which can help to utilize the related knowledge for this task. We select a large corpus from a smart contract community Etherscan.io as our experimental subject. Extensive experimental results show the effectiveness of SCCLLM when compared with baselines in automatic evaluation and human evaluation.
This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.
Sihao Hu, Tiansheng Huang, Fatih İlhan, Selim Furkan Tekin · 5 authors
This paper provides a systematic analysis of the opportunities, challenges, and potential solutions of harnessing Large Language Models (LLMs) such as GPT-4 to dig out vulnerabilities within smart contracts based on our ongoing research. For the task of smart contract vulnerability detection, achieving practical usability hinges on identifying as many true vulnerabilities as possible while minimizing the number of false positives. Nonetheless, our empirical study reveals contradictory yet interesting findings: generating more answers with higher randomness largely boosts the likelihood of producing a correct answer but inevitably leads to a higher number of false positives. To mitigate this tension, we propose an adversarial framework dubbed GPTLens that breaks the conventional one-stage detection into two synergistic stages $-$ generation and discrimination, for progressive detection and refinement, wherein the LLM plays dual roles, i.e., auditor and critic, respectively. The goal of auditor is to yield a broad spectrum of vulnerabilities with the hope of encompassing the correct answer, whereas the goal of critic that evaluates the validity of identified vulnerabilities is to minimize the number of false positives. Experimental results and illustrative examples demonstrate that auditor and critic work together harmoniously to yield pronounced improvements over the conventional one-stage detection. GPTLens is intuitive, strategic, and entirely LLM-driven without relying on specialist expertise in smart contracts, showcasing its methodical generality and potential to detect a broad spectrum of vulnerabilities. Our code is available at: https://github.com/git-disl/GPTLens.
Large Language Models (LLMs), such as ChatGPT, have transformed the field of natural language processing with their capacity for language comprehension and generation of human-like, fluent responses for many downstream tasks. Despite their impressive capabilities, they often fall short in domain-specific and knowledge-intensive domains due to a lack of access to relevant data. Moreover, most state-of-art LLMs lack transparency as they are often accessible only through APIs. Furthermore, their application in critical real-world scenarios is hindered by their proclivity to produce hallucinated information and inability to leverage external knowledge sources. To address these limitations, we propose an innovative system that enhances LLMs by integrating them with an external knowledge management module. The system allows LLMs to utilize data stored in vector databases, providing them with relevant information for their responses. Additionally, it enables them to retrieve information from the Internet, further broadening their knowledge base. The research approach circumvents the need to retrain LLMs, which can be a resource-intensive process. Instead, it focuses on making more efficient use of existing models. Preliminary results indicate that the system holds promise for improving the performance of LLMs in domain-specific and knowledge-intensive tasks. By equipping LLMs with real-time access to external data, it is possible to harness their language generation capabilities more effectively, without the need to continually strive for larger models.
Relation triple extraction (RTE) is an essential task in information extraction and knowledge graph construction. Despite recent advancements, existing methods still exhibit certain limitations. They just employ generalized pre-trained models and do not consider the specificity of RTE tasks. Moreover, existing tagging-based approaches typically decompose the RTE task into two subtasks, initially identifying subjects and subsequently identifying objects and relations. They solely focus on extracting relational triples from subject to object, neglecting that once the extraction of a subject fails, it fails in extracting all triples associated with that subject. To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. Specifically, we design a supervised contrastive learning method that considers multiple positives per anchor rather than restricting it to just one positive. Furthermore, a penalty term is introduced to prevent excessive similarity between the subject and object. Our framework implements taggers in two directions, enabling triples extraction from subject to object and object to subject. Experimental results show that BitCoin achieves state-of-the-art results on the benchmark datasets and significantly improves the F1 score on Normal, SEO, EPO, and multiple relation extraction tasks.
This study provides a comprehensive analysis of the evolving field of Bitcoin research through the application of bibliometric analysis and structural topic modeling techniques. A dataset of 1,937 articles from the Scopus database, spanning the period between 2013 and 2023, was examined, with a specific focus on the prominent digital currency, Bitcoin. The analysis encompasses publication trends, influential journals, authors, institutions, and impactful articles in the field. Through the application of structural topic modeling, six distinct thematic clusters in Bitcoin research are identified, encompassing topics such as blockchain-based digital currency, volatility modeling, portfolio diversification, Bitcoin futures trading, return forecasting, and cryptocurrency regulations. Furthermore, this study outlines future research directions in the domains of finance, economics, and management pertaining to Bitcoin. By bridging the gap in existing literature and providing valuable insights, this study aims to support the development of effective risk management strategies, regulatory frameworks, and business approaches. The findings serve as a valuable resource for industry practitioners, academics, regulators, and policymakers navigating the evolving landscape of cryptocurrencies.
Sharda Kumari, Vipin Kumar, A. Sharmila, C. Ravindra Murthy · 6 authors
Cryptocurrencies have experienced rapid growth and gained popularity worldwide. As a result, social media platforms have become an important source of information for investors and traders seeking to make decisions regarding cryptocurrencies. However, due to the sheer volume of data and the lack of effective analysis techniques, it can be challenging to identify key patterns and trends within social media forums. In this research paper, a blockchain based methodology is proposed for analysis of social media forums for cryptocurrency phase shifts. Specifically, we use sentiment analysis and topic modeling techniques to identify key themes and trends within cryptocurrency social media forums, and then use blockchain technology to verify and authenticate the data. Our results demonstrate that our approach is effective in identifying phase shifts in cryptocurrency markets. In recent years, social media forums have become a hub for discussions on cryptocurrency. The vast amount of data generated in these forums can be analyzed to predict the price shifts of different cryptocurrencies. However, traditional analysis methods may not be efficient enough to extract relevant information from this vast data. Therefore, this research study proposes a blockchain-based analysis approach to social media forums for cryptocurrency phase shifts. The proposed approach uses blockchain to ensure data immutability, and smart contracts to execute the analysis. The results obtained from the analysis can be used for prediction of price shifts of different crypto-currencies.
Nils Augustin, Andreas Eckhardt, Alexander Willem de Jong
Abstract Blockchain technology is argued to drastically change the way we operate within an organizational context, with decentralized autonomous organizations (DAOs) representing a first manifestation of this ongoing trend. DAOs are characterized by an online community that builds the organization’s backbone by providing knowledge and human resources in a transparent, virtual manner, as well as the use of blockchain technology to coordinate their endeavor. Nevertheless, current research highlights the conceptual ambiguity of this emerging phenomenon, leading to potential issues for practitioners and researchers. To provide further clarity on the phenomenon, we study DAOs through the perspective of their members with a two-staged approach by combining elements of a netnographic approach and structural topic modeling. Our findings highlight several contextual features surrounding DAOs, such as their members’ underlying beliefs and views, helping to embed DAOs in existing research streams.
Training and deploying the large language models requires a large mount of computational resource because the language models contain billions of parameters and the text has thousands of tokens. Another problem is that the large language models are static. They are fixed after the training process. To tackle these issues, in this paper, we propose to train and deploy the dynamic large language model on blockchains, which have high computation performance and are distributed across a network of computers. A blockchain is a secure, decentralized, and transparent system that allows for the creation of a tamper-proof ledger for transactions without the need for intermediaries. The dynamic large language models can continuously learn from the user input after the training process. Our method provides a new way to develop the large language models and also sheds a light on the next generation artificial intelligence systems.
Purpose This paper reviews the extant research on Web3.0 published between 2003 and 2022. Design/methodology/approach This study uses a topic modeling procedure latent Dirichlet allocation to uncover the research themes and the key phrases associated with each theme. Findings This study uncovers seven research themes that have been featured in the existing research. In particular, the study highlights the interaction among the research themes that contribute to the understanding of a number of solutions, applications and use cases, such as metaverse and non-fungible tokens. Research limitations/implications Despite the relatively small data size of the study, the results remain significant as they contribute to a more profound comprehension of the relevant field and offer guidance for future research directions. The previous analysis revealed that the current Web3.0 technology is still encountering several challenges. Building upon the pioneering research in the field of blockchain, decentralized networks, smart contracts and algorithms, the study proposes an exploratory agenda for future research from an ecosystem approach, targeting to enhance the current state of affairs. Originality/value Although topics around Web3.0 have been discussed intensively among the crypto community and technological enthusiasts, there is limited research that provides a comprehensive description of all the related issues and an in-depth analysis of their real-world implications from an ecosystem perspective.
Smart contracts are self-executing programs on the blockchain that are critical to a range of industries, including finance, supply chain management, and healthcare.However, comprehending smart contracts can be challenging due to a lack of effective comments in most user-defined code.To address this challenge, we propose a novel retrieval-enhanced approach CC-GRA that leverages retrieval knowledge to generate high-quality comments for Solidity language code.Our approach carefully eliminates duplicated data and template data in the widely-used smart contract dataset to ensure a high-quality corpus.Extensive experiments and comprehensive analysis demonstrate the effectiveness applicability of our approach after being compared with eight state-of-the-art baselines.Finally, we conduct a human study and find the comment quality generated by our approach is better than baselines in terms of similarity, naturalness, and informativeness.
Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations.
Text mining was used to examine the academic publications on blockchain technology and cybersecurity that were published in huge digital libraries. Automated text mining methods like topic modeling and key word extraction are utilized in this literature study to extract topics from a vast corpus of literature. This article emphasizes the cross-disciplinary character of blockchain technology in the context of cybersecurity. The outcomes also show the risks and security holes that emerge as blockchain technology matures. Nevertheless, the investigation this article also outlines research gaps in the area of computer security and recommends research directions for future work that will be necessary to create safe blockchain platforms.
Yusra Mohammed AlRoshdi, Mohammed Al-Badawi, Abdullah Al-Hamdani, Mohamed Sarrab
Decentralized Web (Web3) and Finance (DeFi) have become the main discussion topic in research and industry fields.Cryptocurrencies, as an essential part of DeFi, enjoyed the interest of many stakeholders such as companies, professionals, researchers, and even common citizens eager to benefit from the proposed ecosystems.Although previous research studies focused on establishing price prediction systems using Sentiment Analysis (SA) techniques, the main focus of these studies was the performance of the predictive system rather than the accuracy and efficiency of the used models.In our work, we address two research questions; the predictability of cryptocurrency price based on past social and technical information, and the effect of social features on cryptocurrency price fluctuations using an SA and a Time Series approach.A combination of selected social and technical features was processed and reframed as a prediction problem, then studied to assess the ability of our model to predict the desired price.We noted that there is both an explicit correlation for some considered features and implicit for others, also social features including overall positive and neutral sentiment, and community engagement improved the performance of our model.
Aos Mulahuwaish, Matthew Loucks, Basheer Qolomany, Ala Al‐Fuqaha
Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.
Nowadays, blockchain technology and smart contracts (SCs) spread as hot computer science and finance topics, catching the interest of a heterogeneous range of users. Thinking about blockchain technologies, users can choose between a broad set of alternatives, like Bitcoin, Ethereum, Hyperledger Fabric, Cardano, and many others. The choice of a blockchain platform over another relies on the user’s purpose. For what concerns smart contracts programming, Ethereum is still the most popular platform despite other popular blockchain technologies recently increasing their popularity for SCs development, such as Algorand, Stellar, Hyperledger Fabric, and many others. Such a tremendous number of alternatives makes it difficult to keep track of blockchain trends and use cases of the different technologies. This work explores users’ most discussed topics and opinions about popular blockchain technologies, focusing on smart contracts development, taking advantage of topic modeling techniques, and in particular, taking advantage of the BERT model. The primary data source is Reddit, which was proven to provide different and exciting blockchain topics. The chosen blockchain platform are four of the most popular platforms for SCs development. These technologies include Ethereum, Stellar, Algorand, and Hyperledger Fabric. The analysis of 14.156 sub-reddits highlighted several exciting topics, such as smart contracts auditing, currency inflation, comparisons between crypto, and general blockchain trends. The most present arguments concern Stellar trends, comparisons between Stellar and Ripple, which Wallet to use to store tokens and crypto, SCs auditing, and Ethereum trends.
Non-Fungible Tokens (NFTs) represent deeds of ownership, based on blockchain technologies and smart contracts, of unique crypto assets on digital art forms (e.g., artworks or collectibles). In the spotlight after skyrocketing in 2021, NFTs have attracted the attention of crypto enthusiasts and investors intent on placing promising investments in this profitable market. However, the NFT financial performance prediction has not been widely explored to date. In this work, we address the above problem based on the hypothesis that NFT images and their textual descriptions are essential proxies to predict the NFT selling prices. To this purpose, we propose MERLIN, a novel multimodal deep learning framework designed to train Transformer-based language and visual models, along with graph neural network models, on collections of NFTs' images and texts. A key aspect in MERLIN is its independence on financial features, as it exploits only the primary data a user interested in NFT trading would like to deal with, i.e., NFT images and textual descriptions. By learning dense representations of such data, a price-category classification task is performed by MERLIN models, which can also be tuned according to user preferences in the inference phase to mimic different risk-return investment profiles. Experimental evaluation on a publicly available dataset has shown that MERLIN models achieve significant performances according to several financial assessment criteria, fostering profitable investments, and also beating baseline machine-learning classifiers based on financial features.
Vikram Puri, Subhra R. Mondal, Subhankar Das, Vasiliki Vrana
Blockchain and immersive technology are the pioneers in bringing digitalization to tourism, and researchers worldwide are exploring many facets of these techniques. This paper analyzes the various aspects of blockchain technology and its potential use in tourism. We explore high-frequency keywords, perform network analysis of relevant publications to analyze patterns, and introduce machine learning techniques to facilitate systematic reviews. We focused on 94 publications from Web Science that dealt with blockchain implementation in tourism from 2017 to 2022. We used Vosviewer for network analysis and artificial intelligence models with the help of machine learning tools to predict the relevance of the work. Many reviewed articles mainly deal with blockchain in tourism and related terms such as smart tourism and crypto tourism. This study is the first attempt to use text analysis to improve the topic modeling of blockchain in tourism. It comprehensively analyzes the technology’s potential use in the hospitality, accommodation, and booking industry. In this context, the paper provides significant value to researchers by giving an insight into the trends and keyword patterns. Tourism still has many unexplored areas; journal articles should also feature special studies on this topic.
Open access
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification