Well labeled natural language corpus data is essential for most natural language processing techniques, especially in specialized fields. However, cohort biases remain a significant challenge in machine learning. The narrow origin of data sampling or human annotators in cohorts is a prevalent issue for machine learning researchers due to its potential to induce bias in the final product. During the development of the CryptoLin corpus for another research project, the authors became concerned about the potential influence of cohort bias on the selection of annotators. Therefore, this paper addresses the question of whether cohort diversity improves the labeling result through the implementation of a repeated annotator process, involving two annotator cohorts and a statistically robust comparison methodology. The utilization of statistical tests, such as the Chi-Square Independence test for absolute frequency tables, and the construction of confidence intervals for Kappa point estimates, facilitates a rigorous analysis of the differences between Kappa estimates. Furthermore, the application of a two-proportion z-test to compare the accuracy scores of UTAD and IE annotators for various pre-trained models, including Vader Sentiment Analysis, TextBlob Sentiment Analysis, Flair NLP library, and FinBERT Financial Sentiment Analysis with BERT, contributes to the advancement of knowledge in this field. The paper utilizes Cryptocurrency Linguo (CryptoLin), a corpus containing 2683 cryptocurrency-related news articles spanning more than three years,and compares two different selection criteria for the annotators. CryptoLin was annotated twice with discrete values representing negative, neutral, and positive news respectively. The first annotation was done by twenty-seven annotators from the same cohort. Each news title was randomly assigned and blindly annotated by three human annotators. The second annotation was carried out by eighty-three annotators from three cohorts. Each news title was randomly assigned and blindly annotated by three human annotators, one in each different cohort. In both annotations, a consensus mechanism using simple voting was applied. The first annotation used the same cohort with students from the same nationality and background. The second used three cohorts with students from a very diverse set of nationalities and educational backgrounds. The results demonstrate that manual labeling done by both groups was acceptable according to inter-rater reliability coefficients Fleiss’s Kappa, Krippendorff’s Alpha, and Gwet’s AC1. Preliminary analysis utilizing Vader, Textblob, Flair, and FinBERT confirmed the utility of the data set labeling for further refinement of sentiment analysis algorithms. Our results also highlight that the more diverse annotator pool performed better in all measured aspects.
The rapid growth of the cryptocurrency market has led to an increasing interest in the subject. Cryptocurrency is now recognized as an asset, and laws and financial regulations have begun to emerge for supporting its practical use. As a result, it has become essential to perform data mining and attain knowledge from text data related to cryptocurrency. Previous studies have focused on analyzing data from a single source such as Twitter. However, there are unique insights to be gained from data across multiple platforms. In the present study, we utilized data mining techniques to extract insights from LexisNexis, Web of Science, and Reddit, representing the media, academia, and general public, respectively. Among unsupervised learning technologies, topic modeling was employed for the analysis. Topic modeling is a methodology that uncovers hidden meanings within the collected data. Among the diverse topic modeling techniques available, bidirectional encoder representations from transformers topic was chosen for the analysis. BERTopic considered to be state-of-the-art in the field of topic modeling. Dynamic topic modeling was employed to track changes in themes over time. Our experimental results reveal a tendency in the news to cover major events related to cryptocurrencies, such as regulatory developments and market trends. Academic papers, on the other hand, tend to focus on the technology behind cryptocurrencies and related research. Finally, social media conversations center more around information delivery from an investor’s psychological perspective, such as market sentiment and investment strategies.
This paper examines existing Web3.0 research published between 2003 and 2022. We uncover seven research themes that have been featured in previous research, as well as the key phrases associated with each theme, using a topic modeling procedure called Latent Dirichlet Allocation (LDA). We emphasize the interaction of research themes that contribute to the understanding of a variety of solutions, applications, and use cases, such as metaverse and Non-fungible tokens (NFT). We conclude by proposing an exploratory agenda for future research based on pioneering research in the fields of Blockchain, decentralized networks, smart contracts, and algorithms.
대체불가토큰(Non-Fungible Token: NFT)은 현재 예술, 엔터테인먼트, 게임을 중심으로 급성장하고 있으며, 메타버스를 중심으로 패션, 마케팅 산업에도 적극적으로 도입되고 있다. NFT는 이제 산업이 형성되고 있어 NFT와 관련된 연구는 아직 탐색적인 수준이다. 본 연구에서는 2018년부터 2022년 6월까지 출간된 NFT 관련 국내외 논문 각각 95편, 153편의 서지정보를 LDA 기법으로 토픽모델링을 수행하였다. 이를 통해 키워드간 관계성, NFT 관련 분야 전체 연구의 구조를 살펴보고 국내외 NFT 관련 연구 주제의 유사점과 차이점을 비교하였으며, 분석결과와 선행연구를 기반으로 NFT 분야의 향후 연구 방향을 제시하였다. 본 연구는 NFT 관련 국내외 연구를 통합하여 진행한 첫 리뷰 연구로서 의의가 있으며 실무적으로 NFT 산업 초기 단계에 정부가 관련 정책 수립하는데 현황을 파악하고, 정부의 역할을 인지하는데 기여할 것으로 기대한다.
Open access
Diverse Topics in Contemporary Research
Technology and Data Analysis
Diverse Approaches in Healthcare and Education Studies
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.
We are presenting a fast and innovative approach to performing approximate pattern-matching for plagiarism detection, using an NDFA-based approach that significantly enhances performance compared to other existing similarity measures. We outline the advantages of our approach in the context of blockchain-based non-fungible tokens (NFTs). We present, formalize, discuss and test our proposed approach in several real-world scenarios and with different similarity measures commonly used in plagiarism detection, and observe significant throughput enhancements throughout the entire spectrum of tests, with little to no compromises on the accuracy of the detection process overall. We conclude that our approach is suitable and adequate to perform approximate pattern-matching for plagiarism detection, and outline research directions for future improvements.
Understanding the semantic of a collection of texts is a challenging task. Topic models are probabilistic models that aims at extracting "topics" from a corpus of documents. This task is particularly difficult when the corpus is composed of short texts, such as posts on social networks. Following several previous research papers, we explore in this paper a set of collected tweets about bitcoin. In this work, we train three topic models and evaluate their output with several scores. We also propose a concrete application of the extracted topics.
Smart Contracts are general-purpose programs that provide a higher level of security than traditional contracts and reduce other transaction costs associated with the bargaining practice, as they are executed in a Blockchain infrastructure. Developers use smart contracts to build their tokens and set up gambling games, crowd sales, ICO, and many others domains of application. The security of Smart Contracts is also crucial, as SCs at the very core level, move money. In recent years, researchers have provided a set of known vulnerabilities that afflict SCs. This study analyzed the relationship between the SC domain of application, domain category, and known vulnerabilities. We categorized the SC using the topic modeling on a curated dataset of SC annotated with know vulnerabilities. Indeed, we found that a certain category of SC is strongly associated with specific vulnerabilities.
Abstract Technological innovation generates products, services, and processes that can disrupt existing industries and lead to the emergence of new fields. Distributed ledger technology, or blockchain, offers novel transparency, security, and anonymity characteristics in transaction data that may disrupt existing industries. However, research attention has largely examined its application to finance. Less is known of any broader applications, particularly in Industry 4.0. This study investigates academic research publications on blockchain and predicts emerging industries using academia‐industry dynamics. This study adopts latent Dirichlet allocation and dynamic topic models to analyze large text data with a high capacity for dimensionality reduction. Prior studies confirm that research contributes to technological innovation through spillover, including products, processes, and services. This study predicts emerging industries that will likely incorporate blockchain technology using insights from the knowledge structure of publications.
Non-fungible token (NFT) trade has grown drastically over recent years. While scholarship on the technical aspects and potential applications of NFTs has been steadily increasing, less attention has been directed to the human perception of or attitudes toward this new type of digital asset. The aim of this research is to investigate what concerns are expressed in relation to non-fungible tokens by those who engage with NFTs on the social media platform Twitter. In this study, data was gathered through online social media data mining of NFT-related posts on Twitter. Two datasets (with 18,373 and 36,354 individual tweet records, respectively) were obtained. Topic modeling was used as a method of data analysis. Our results reveal 19 overall themes of concerns around NFTs as expressed on Twitter, which broadly fall into two categories: concerns about attacks and threats by third parties; and concerns about trading and the role of marketplaces. Overall, this study offers a better understanding of the expressions of concern, uncertainty, and the perception of possible barriers related to NFT trading. These findings contribute to theoretical insight and can, moreover, function as a basis for developing practical design and policy interventions.
Open access
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
Semantic code search technology allows searching for existing code snippets through natural language, which can greatly improve programming efficiency. Smart contracts, programs that run on the blockchain, have a code reuse rate of more than 90%, which means developers have a great demand for semantic code search tools. However, the existing code search models still have a semantic gap between code and query, and perform poorly on specialized queries of smart contracts. In this paper, we propose a Multi-Modal Smart contract Code Search (MM-SCS) model. Specifically, we construct a Contract Elements Dependency Graph (CEDG) for MM-SCS as an additional modality to capture the data-flow and control-flow information of the code. To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features. In addition, we use a fine-tuned pretrained model to ensure the model's effectiveness when the training data is small. We compared MM-SCS with four state-of-the-art models on a dataset with 470K (code, docstring) pairs collected from Github and Etherscan. Experimental results show that MM-SCS achieves an MRR (Mean Reciprocal Rank) of 0.572, outperforming four state-of-the-art models UNIF, DeepCS, CARLCS-CNN, and TAB-CS by 34.2%, 59.3%, 36.8%, and 14.1%, respectively. Additionally, the search speed of MM-SCS is second only to UNIF, reaching 0.34s/query.
We focus on a type of linguistic formal reasoning where the goal is to reason\nover explicit knowledge in the form of natural language facts and rules (Clark\net al., 2020). A recent work, named PRover (Saha et al., 2020), performs such\nreasoning by answering a question and also generating a proof graph that\nexplains the answer. However, compositional reasoning is not always unique and\nthere may be multiple ways of reaching the correct answer. Thus, in our work,\nwe address a new and challenging problem of generating multiple proof graphs\nfor reasoning over natural language rule-bases. Each proof provides a different\nrationale for the answer, thereby improving the interpretability of such\nreasoning systems. In order to jointly learn from all proof graphs and exploit\nthe correlations between multiple proofs for a question, we pose this task as a\nset generation problem over structured output spaces where each proof is\nrepresented as a directed graph. We propose two variants of a proof-set\ngeneration model, multiPRover. Our first model, Multilabel-multiPRover,\ngenerates a set of proofs via multi-label classification and implicit\nconditioning between the proofs; while the second model, Iterative-multiPRover,\ngenerates proofs iteratively by explicitly conditioning on the previously\ngenerated proofs. Experiments on multiple synthetic, zero-shot, and\nhuman-paraphrased datasets reveal that both multiPRover models significantly\noutperform PRover on datasets containing multiple gold proofs.\nIterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios\nwhere all examples have single correct proofs. It also generalizes better to\nquestions requiring higher depths of reasoning where multiple proofs are more\nfrequent. Our code and models are publicly available at\nhttps://github.com/swarnaHub/multiPRover\n
Code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code. However, high-quality code comments are often unavailable in smart contracts, the increasingly popular programs that run on the blockchain. In this paper, we propose a Multi-Modal Transformer-based (MMTrans) code summarization approach for smart contracts. Specifically, the MMTrans learns the representation of source code from the two heterogeneous modalities of the Abstract Syntax Tree (AST), i.e., Structure-based Traversal (SBT) sequences and graphs. The SBT sequence provides the global semantic information of AST, while the graph convolution focuses on the local details. The MMTrans uses two encoders to extract both global and local semantic information from the two modalities respectively, and then uses a joint decoder to generate code comments. Both the encoders and the decoder employ the multi-head attention structure of the Transformer to enhance the ability to capture the long-range dependencies between code tokens. We build a dataset with over 300K pairs of smart contracts, and evaluate the MMTrans on it. The experimental results demonstrate that the MMTrans outperforms the state-of-the-art baselines in terms of four evaluation metrics by a substantial margin, and can generate higher quality comments.
Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.
Jan 1, 2021·Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules A recent work, named PROVER However, compositional reasoning is not always unique and there may be multiple ways of reaching the correct answer. Thus, in our work, we address a new and challenging problem of generating multiple proof graphs for reasoning over natural language rule-bases. Each proof provides a different rationale for the answer, thereby improving the interpretability of such reasoning systems. In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph. We propose two variants of a proof-set generation model, MULTIPROVER. Our first model, Multilabel-MULTIPROVER, generates a set of proofs via multi-label classification and implicit conditioning between the proofs; while the second model, Iterative-MULTIPROVER, generates proofs iteratively by explicitly conditioning on the previously generated proofs. Experiments on multiple synthetic, zero-shot, and human-paraphrased datasets reveal that both MULTIPROVER models significantly outperform PROVER on datasets containing multiple gold proofs. Iterative-MULTIPROVER obtains state-of-the-art proof F1 in zero-shot scenarios where all examples have single correct proofs. It also generalizes better to questions requiring higher depths of reasoning where multiple proofs are more frequent.
Tsz Hon Yuen, Muhammed F. Esgin, Joseph K. Liu, Man Ho Au · 5 authors
We introduce a novel generic ring signature construction, called DualRing, which can be built from several canonical identification schemes (such as Schnorr identification). DualRing differs from the classical ring signatures by its formation of two rings: a ring of commitments and a ring of challenges. It has a structural difference from the common ring signature approaches based on accumulators or zero-knowledge proofs of the signer index. Comparatively, DualRing has a number of unique advantages.
Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.
Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining.
Jan 1, 2020·Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences
Given the increasing interest in blockchain technology, we present a large-scale cross-disciplinary literature analysis of research on the blockchain using topic modelling with the goal of identifying the major research trends, research methodologies, and fruitful areas for further research. In particular, the analysis focuses on abstracting out research trends from relevant terms and topics related to the research disciplines of Business, Computer Science, Economics, Social Sciences, Engineering, Healthcare, and Law. A total of 2,125 articles published between 2008 to up until early 2019 in academic journals and conferences were analyzed. Results of our analysis reveal that research is bipartite between practical and research domains, with academic research on blockchain not clearly aligning with organizational and social benefits. Also, we found – 1) few inter-disciplinary publications, and 2) a small number of studies that use surveys, experiments, and case studies as their research method. Our findings also reveal that research on Blockchain in the social sciences and law is still in the embryonic stage, thus making it essential to develop more direct research efforts for Blockchain to thrive in all research disciplines.
shows that transformers can act as "soft theorem provers" by answering questions over explicitly provided knowledge in natural language. In our work, we take a step closer to emulating formal theorem provers, by proposing PROVER, an interpretable transformer-based model that jointly answers binary questions over rule-bases and generates the corresponding proofs. Our model learns to predict nodes and edges corresponding to proof graphs in an efficient constrained training paradigm. During inference, a valid proof, satisfying a set of global constraints is generated. We conduct experiments on synthetic, hand-authored, and human-paraphrased rule-bases to show promising results for QA and proof generation, with strong generalization performance. First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation). Second, when trained on questions requiring lower depths of reasoning, it generalizes significantly better to higher depths (up to 15% improvement). Third, PROVER obtains near perfect QA accuracy of 98% using only 40% of the training data. However, generating proofs for questions requiring higher depths of reasoning becomes challenging, and the accuracy drops to 65% for "depth 5", indicating significant scope for future work.