Abstract Environmental transition is increasingly governed through multilevel systems in which authority is shared across supranational, national, and regional governments. Existing research on multilevel climate governance has focused on coordination, implementation, and compliance, largely treating environmental objectives as politically consistent among territorial levels once adopted. This paper argues that multilevel governance also reshapes the political content of environmental transition itself, a process we call political reinterpretation: common climate objectives are selectively reprioritized and reframed as they enter territorially distinct political arenas. We test this argument using text analysis of parliamentary discourse, applying structural topic modeling to 10,564 speeches delivered across Spain’s seventeen Autonomous Communities (ACs) between 2019 and 2024 to uncover five substantive dimensions of environmental-transition discourse directly from legislative text. We find that these dimensions are distributed unevenly across regions, and, more critically, that Spain’s major statewide parties do not reproduce their national environmental-transition profiles across territories: territorial variation in the topics they prioritize within each party systematically exceeds the variation observed between parties operating in the same region. This pattern holds even among parties whose organizational structure gives them a strong incentive toward national uniformity, indicating that territorial incentives can outweigh the integrative pressures of statewide party organization. Understanding climate governance in decentralized systems therefore requires attention not only to how environmental policy is implemented across levels of government, but to how its political meaning is reconstructed as authority becomes territorially dispersed.
This study proposes a structural model for understanding digital trust in smart-market environments by comparing the market-based trust architecture of Korea and the state-based trust architecture of China. Although both countries rely on similar technological foundations—blockchain, data infrastructure, AI systems, and CBDC—their institutional path dependencies and regulatory philosophies have produced divergent trust mechanisms. To explain these differences, the study introduces the 4-Layer Trust Architecture (4LTA–Seo), comprising incentives, rule enforcement, verification (data/AI), and institutional linkage. This framework conceptualizes tokens as digital institutions that integrate these layers to automate trust formation and oversight.Methodologically, the research applies Qualitative Comparative Analysis (QCA) using policy documents, technical whitepapers, and regulatory texts from both countries. It incorporates Zhang & Wang’s DTI (Data–Algorithm–Risk–Privacy) framework to compare how information architectures shape verification dynamics and trust costs. The study analyzes how institutional configurations rearrange the weighting and function of each trust layer, producing different stability and cost outcomes.Findings are expected to show that Korea’s market-driven architecture emphasizes incentives and behavioral inducement, while China’s state-driven model prioritizes rule enforcement and systemic integration. The research clarifies how tokens function as "units of trust" only when embedded within institutionally coherent architectures. Ultimately, the study offers structural insights for reinstitutionalizing trust in digital systems, with implications for Web3 governance, CBDC design, and digital public administration.
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.
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.
How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance. This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos. We conducted a two-step study: interviews with 10 journalists established a baseline understanding of expert video interpretation; 114 individuals through deliberation using InclusiveAI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings reveal distinct differences in interpretative priorities: while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity, and emotional neutrality. Furthermore, we examined how different governance mechanisms - quadratic vs. weighted voting and equal vs. 20/80 voting power - shape users' decision-making regarding AI behavior. Results indicate that voting methods significantly influence outcomes, with quadratic voting reinforcing perceptions of liberal democracy and political equality. Our study underscores the necessity of selecting appropriate governance mechanisms to better capture user perspectives and suggests decentralized AI governance as a potential way to facilitate broader public engagement in AI development, ensuring that varied perspectives meaningfully inform design decisions.
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.
Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5's legal reasoning and ChatGPT's legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5's legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors' decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT's drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM's legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.
Lubdhak Mondal, Udeshya Raj, S Abinandhan, Began Gowsik S · 6 authors
This study investigates the relationship between narratives conveyed through microblogging platforms, namely Twitter, and the value of crypto assets. Our study provides a unique technique to build narratives about cryptocurrency by combining topic modelling of short texts with sentiment analysis. First, we used an unsupervised machine learning algorithm to discover the latent topics within the massive and noisy textual data from Twitter, and then we revealed 4-5 cryptocurrency-related narratives, including financial investment, technological advancement related to crypto, financial and political regulations, crypto assets, and media coverage. In a number of situations, we noticed a strong link between our narratives and crypto prices. Our work connects the most recent innovation in economics, Narrative Economics, to a new area of study that combines topic modelling and sentiment analysis to relate consumer behaviour to narratives.
In this study, I examine how an online cryptocurrency community discusses the issue of climate change. In particular, I examine distinctive themes present within discussions that occur on the r/CryptoCurrency forum hosted by reddit.com. Existing research has demonstrated that there are significant carbon emissions linked to cryptocurrency. However, cryptocurrency primarily exists as a peer-to-peer system, meaning that the individual perceptions of cryptocurrency adopters may provide insight into how to address the emissions problem. Using latent Dirichlet allocation and publicly available textual data from Reddit, I find that Reddit's cryptocurrency community engages in robust discussions pertaining to the energy needed to power cryptocurrency systems, most of which is generated from fossil fuels. Therefore, the discussions identified in this study suggest that the social aspect of cryptocurrency may be important when examining the links between cryptocurrency and climate change since they help identify what subjects related to climate change are important for this community.
This research aims to develop a tool to assess the impact of social media on the market by collecting and analyzing cryptocurrency-related tweets on Twitter.Python and relevant libraries are utilized for scraping tweets and cryptocurrency trading data, followed by data preprocessing.The VADER model is then employed for sentiment analysis of the tweets, extracting sentiment polarity and intensity.Considering the influence of tweets, an overall sentiment score is calculated.
The collection of technologies related to Web3 will have dramatic effects on advertising and public relations research, theory, and practice. NFTs and cryptocurrencies are exemplar technologies that are already being used in innovative marketing efforts. This paper discusses Web3 from an advertising-centric point of view. We predict several effects, including a rise in scarcity appeals (but declining effectiveness), an exponential increase in word-of-mouth marketing, and the fading importance of overlapping groups of consumers. We also provide two case studies to contextualize our predictions. Implications for the future of advertising theory and research are discussed throughout.
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.
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.