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Content, formats and licensing of datasets from autonomous communities: Value generation, sustainability and proposals for improvement 自治区数据集的内容、格式和许可:价值产生、可持续性和改进建议
Pub Date : 2025-08-25 DOI: 10.1016/j.jjimei.2025.100369
Ricardo Curto-Rodríguez, Rafael Marcos-Sánchez, Daniel Ferrández
Open government data (OGD) initiatives, established at all levels of public administration and globally, have significant potential for value generation. However, their actual implementation often reveals significant shortcomings that hinder their potential for value creation. This study addresses a critical gap in the literature by evaluating the design of OGD policies in Spain, focusing specifically on the industrial sector at the autonomous community level. The research assesses the available data's content, formats, and licensing through a population-based analysis of all datasets labeled under the “industry” category across the 17 Spanish autonomous communities. The findings reveal a fragmented and inconsistent landscape: of over 46,000 datasets published by autonomous community governments, only 532 were initially labeled as industry-related, and after a rigorous selection process—removing duplicates, outdated records, and mislabeling entries—only 316 were deemed valid. The study highlights a predominance of non-reusable formats such as HTML and a lack of standardisation in the categorization of information. While most datasets use open licenses (mainly Creative Commons BY), the variability in download options and formats limits their automated processing and reuse. These results underscore the need for standardization criteria, improved data quality, and strategic alignment of OGD initiatives with sectoral priorities such as industrial competitiveness and sustainability. The paper concludes with four contributions to enhance coherence, usability, and impact of open industrial data, aiming to support OGD policymaking and foster innovation ecosystems at the autonomous community level.
在各级公共行政和全球范围内建立的开放政府数据(OGD)倡议具有创造价值的巨大潜力。然而,它们的实际实现经常暴露出阻碍其价值创造潜力的重大缺陷。本研究通过评估西班牙OGD政策的设计,特别关注自治社区层面的工业部门,解决了文献中的一个关键空白。该研究通过对17个西班牙自治区的“行业”类别下的所有数据集进行基于人口的分析,评估可用数据的内容、格式和许可。调查结果揭示了一个支离破碎和不一致的格局:在自治区政府发布的46,000多个数据集中,只有532个最初被标记为与行业相关,经过严格的选择过程-删除重复,过时的记录和错误标记的条目-只有316个被认为是有效的。该研究强调了不可重用格式(如HTML)占主导地位,以及信息分类缺乏标准化。虽然大多数数据集使用开放许可(主要是Creative Commons BY),但下载选项和格式的可变性限制了它们的自动处理和重用。这些结果强调了标准化标准、改进数据质量以及OGD计划与行业优先事项(如工业竞争力和可持续性)的战略一致性的必要性。本文最后提出了四项贡献,以增强开放工业数据的一致性、可用性和影响力,旨在支持OGD政策制定,并在自治社区层面培育创新生态系统。
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引用次数: 0
Green research and development capacity and corporate environmental responsibility in the pursuit of green product innovation: a bibliometric analysis 追求绿色产品创新中的绿色研发能力与企业环境责任:文献计量学分析
Pub Date : 2025-08-20 DOI: 10.1016/j.jjimei.2025.100367
Jakeline Serrano-García , Juan José Arbeláez-Toro , José Daniel Cardona-Cárdenas , Frederic Marimon
Considering the state of the art and to our knowledge, there have been no bibliometric studies analyzing the relationship between Green Research and Development (GR&DC) Capacity and Corporate Environmental Responsibility (CER) to promote green innovative products (GPI) and financial sustainability in manufacturing companies. This study aims at identifying behavioral patterns, scientific trends and future work regarding the association of the proposed topics. To perform the bibliometric analysis, 3,473 records from the Scopus database were collected. The VOSviewer software version 1.6.20 has been used and through it, a significant growth in the literature since 2012, has been observed. The results evidenced that China stands out as the lead country in document production, followed by the United Kingdom and India. 3,365 authors have contributed to knowledge. It is evident that China has the largest number of representing universities. Among the analyzed trends, the authors reveal that, the combination of GR&DC and CER drives the creation of GPI, improving financial sustainability through investments in green development, energy efficiency and emission reduction projects. It was identified that GPI integrates sustainable strategies that generate environmental value, improve organizational performance and strengthen competitiveness. Similarly, CER also plays a key role in improving the sustainability of manufacturing companies, promoting green innovation and financial strategies. Therefore, the results of this study offer scholars an understanding of the trends and critical points on the topics discussed, as well as the recommendations to continue a series of future research lines identified in this work.
考虑到目前的技术水平和我们所知,还没有文献计量学研究分析绿色研发(gr&dc)能力和企业环境责任(CER)之间的关系,以促进绿色创新产品(GPI)和制造企业的财务可持续性。这项研究的目的是确定行为模式,科学趋势和未来的工作有关的协会提出的主题。为了进行文献计量学分析,从Scopus数据库中收集了3,473条记录。已使用VOSviewer软件版本1.6.20,并通过它,观察到自2012年以来文献的显着增长。结果表明,中国是文献产出的主要国家,其次是英国和印度,有3365位作者贡献了知识。很明显,中国拥有最多的代表性大学。在分析的趋势中,作者发现GR&;DC和CER的结合推动了GPI的创建,通过对绿色发展、能效和减排项目的投资提高了金融可持续性。GPI整合了产生环境价值、提高组织绩效和加强竞争力的可持续战略。同样,CER在提高制造企业的可持续性、促进绿色创新和金融战略方面也发挥着关键作用。因此,本研究的结果为学者们提供了对所讨论主题的趋势和关键点的理解,以及继续本工作中确定的一系列未来研究方向的建议。
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引用次数: 0
A structural topic modeling of communication research: insights from over a century of journals' abstracts 传播学研究的结构性主题模型:来自一个多世纪期刊摘要的见解
Pub Date : 2025-08-18 DOI: 10.1016/j.jjimei.2025.100364
Mohamed M. Mostafa , Mohammad Alhur , Ahmed M. Moustafa
Communication research is a broad and interdisciplinary field that is strongly influenced by several other disciplines, including behavioral and human sciences. This study uses structural topic modeling (STM) to analyze and trace the intellectual structure of the field over the past century based on a twenty-nine communication journals’ corpus encompassing 24,983 abstracts, totaling more than two million words. Results show a wide range of important research themes in the field, including communication theory, media analysis, health communication, rhetorical theory, interpersonal interactions, small group communications, political debate, and speech education. Diachronically, our results reveal that some topics, such as “speech education” and “political debate” have waned over time, whereas other topics, such as “narrative/discourse analysis” and “global policy change” have gained recently more attention from communication scholars. These findings underscore not only the intellectual breadth and historical evolution of communication research but also highlight key paradigm shifts in the field. The study demonstrates how computational text analysis can inform meta-theoretical understanding and strategic planning within academic disciplines.
传播学研究是一个广泛的跨学科领域,受到其他几个学科的强烈影响,包括行为科学和人文科学。本研究基于29种传播期刊的语料库,共包含24,983篇摘要,总计超过200万字,使用结构主题模型(STM)分析和追踪过去一个世纪该领域的智力结构。结果显示了该领域广泛的重要研究主题,包括传播理论,媒体分析,健康传播,修辞理论,人际互动,小团体传播,政治辩论和语言教育。从历时上看,我们的研究结果表明,一些话题,如“言语教育”和“政治辩论”随着时间的推移而减弱,而其他话题,如“叙事/话语分析”和“全球政策变化”最近受到了传播学者的更多关注。这些发现不仅强调了传播研究的知识广度和历史演变,而且强调了该领域的关键范式转变。该研究展示了计算文本分析如何为学科内的元理论理解和战略规划提供信息。
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引用次数: 0
Conceptualization and validation of an intelligent digital twin design framework for supply chain risk management 供应链风险管理智能数字孪生设计框架的概念化与验证
Pub Date : 2025-08-18 DOI: 10.1016/j.jjimei.2025.100365
Matteo Gabellini, Alberto Regattieri, Marco Bortolini, Michele Ronchi
Intelligent digital twins for supply chain risk management have recently gained attention due to rising disruptions, increasing supply chain complexity, and the need for advanced tools. Although various frameworks exist, few clearly identify the necessary data, predictions, and decision-making problems for their development, and even fewer have been validated in real-world case studies. This study fills those gaps by proposing and validating a comprehensive design framework in the automotive sector. The results show that the prototypes developed based on the framework effectively support tasks such as predicting supply chain performance and guiding supplier selection and order allocation while significantly reducing the time needed for risk management tasks.
由于中断的增加、供应链复杂性的增加以及对先进工具的需求,用于供应链风险管理的智能数字孪生体最近受到了关注。尽管存在各种框架,但很少有框架清楚地确定开发所需的数据、预测和决策问题,在实际案例研究中得到验证的框架就更少了。本研究通过提出和验证汽车行业的综合设计框架来填补这些空白。结果表明,基于该框架开发的原型能够有效地支持预测供应链绩效、指导供应商选择和订单分配等任务,同时显著缩短了风险管理任务所需的时间。
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引用次数: 0
Mitigating uncertainty in travel agency selection in Jordan: A signaling theory approach 减轻约旦旅行社选择的不确定性:一个信号理论方法
Pub Date : 2025-08-02 DOI: 10.1016/j.jjimei.2025.100362
Fadi Herzallah , Bashar Alhaj Mohammad , Mahmoud Alhayek , Syed Md. Faisal Ali Khan
Uncertainty plays a critical role in tourism purchase decisions, particularly in developing markets. This study investigates how Jordanian tourists reduce uncertainty when buying package tours by applying signaling theory. A questionnaire was distributed to 450 tourists with prior travel agency experience, yielding 376 valid responses analyzed using PLS-SEM and IPMA. Results show that information quality, popularity, positive comments, and reputation significantly reduce uncertainty about travel agencies, while return policy has no significant effect. Moreover, uncertainty negatively influences purchase decisions. IPMA findings highlight that reputation is the most important factor in reducing uncertainty, followed by popularity and positive comments. Although return policy scores highest in performance, it has the least impact on uncertainty. This study contributes to tourism literature by identifying specific customer-seller signals that influence perceived uncertainty and by clarifying the link between uncertainty reduction and purchase behavior in the context of travel agencies in developing countries.
不确定性在旅游购买决策中起着关键作用,特别是在发展中市场。本研究运用信号理论探讨约旦游客在购买旅行团时如何减少不确定性。对450名有旅行社经验的游客进行问卷调查,得到376份有效回复,使用PLS-SEM和IPMA进行分析。结果表明,信息质量、知名度、正面评价和声誉显著降低了旅行社的不确定性,而退票政策没有显著影响。此外,不确定性负向影响购买决策。IPMA的调查结果强调,声誉是减少不确定性的最重要因素,其次是受欢迎程度和积极评价。尽管返回策略在性能上得分最高,但它对不确定性的影响最小。本研究通过识别影响感知不确定性的特定客户-卖家信号,并通过澄清发展中国家旅行社背景下不确定性减少与购买行为之间的联系,为旅游文献做出了贡献。
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引用次数: 0
Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decomposition and CNN-based feature extraction 时间数据的小波- cnn:通过多分辨率小波分解和基于cnn的特征提取增强长期股票价格预测
Pub Date : 2025-07-26 DOI: 10.1016/j.jjimei.2025.100360
Komei Hiruta , Junsuke Senoguchi
The global economy relies heavily on stock markets, making accurate stock price predictions essential for academic research and practical applications. The task of predicting stock prices presents significant challenges due to the non-linear relationships between historical and future values and the multitude of factors influencing price fluctuations. To address these challenges, we propose an approach that combines wavelet transformation and a convolutional neural network (CNN), both of which are specialized for long-term stock price prediction, to efficiently and automatically extract the features of stock prices at various temporal resolutions. Specifically, we first acquire components with different temporal resolutions using wavelet transform, then convert the wavelet-transformed data into images, and finally perform CNN processing to automatically extract useful temporal features for prediction. Experimental results demonstrate that our method achieves a higher prediction accuracy than conventional machine learning methods, especially in long-term predictions.
全球经济在很大程度上依赖于股票市场,准确的股票价格预测对于学术研究和实际应用至关重要。由于历史和未来价值之间的非线性关系以及影响价格波动的众多因素,预测股票价格的任务提出了重大挑战。为了解决这些挑战,我们提出了一种结合小波变换和卷积神经网络(CNN)的方法,这两种方法都是专门用于长期股票价格预测的,以有效和自动地提取不同时间分辨率下的股票价格特征。具体而言,我们首先使用小波变换获取不同时间分辨率的分量,然后将小波变换后的数据转换成图像,最后进行CNN处理,自动提取有用的时间特征进行预测。实验结果表明,该方法比传统的机器学习方法具有更高的预测精度,特别是在长期预测中。
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引用次数: 0
Blockchain technology to improve traceability in the coffee supply chain: A systematic literature review 区块链技术提高咖啡供应链的可追溯性:系统的文献综述
Pub Date : 2025-07-24 DOI: 10.1016/j.jjimei.2025.100359
Christian Gómez, Benoit Garbinato
Coffee is consumed worldwide, with its supply chain starting with coffee growers, who benefit least from it. Across its production, distribution, and commercialization processes, there are risks and issues that could damage the safety and authenticity of this product. Therefore, the coffee industry is looking for innovative technologies that allow traceability in the coffee supply chain. In this context, blockchain technology offers a promising solution as it supports traceability via a decentralized system that allows immutable records and transparent access; it also promotes collaborative work and removes intermediaries by generating trust between participants. This systematic literature review describes the state-of-the-art in research and development about the use of blockchain technology to improve traceability in the coffee supply chain. We also outline the open challenges that remain to be addressed in this field. We use the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to achieve this goal. Our findings suggest that the developments are mainly conceptual designs and prototypes, focusing on tracing products and verifying their authenticity using the Ethereum or Hyperledger blockchains. Also, our results show various challenges on the technology side, like efficiency improvements, integration with other technologies, infrastructure, and a lack of standards. There are also challenges at the management level, like the necessity of agreements for traceability processes, data governance, willingness to invest and pay, education, and support to deploy the technology on farms. After overcoming these open challenges, blockchain technology can improve traceability and increase value for stakeholders in the coffee supply chain.
咖啡在世界范围内消费,其供应链从咖啡种植者开始,他们从中受益最少。在其生产、分销和商业化过程中,存在可能损害该产品安全性和真实性的风险和问题。因此,咖啡行业正在寻找创新技术,以实现咖啡供应链的可追溯性。在这种情况下,区块链技术提供了一个很有前途的解决方案,因为它通过一个分散的系统支持可追溯性,允许不可变的记录和透明的访问;它还促进协作工作,并通过在参与者之间建立信任来消除中介。这篇系统的文献综述描述了使用区块链技术来提高咖啡供应链可追溯性的研究和开发的最新进展。我们还概述了在这一领域仍有待解决的公开挑战。我们使用首选报告项目进行系统评价和荟萃分析(PRISMA)方法来实现这一目标。我们的研究结果表明,这些开发主要是概念设计和原型,重点是使用以太坊或超级账本区块链跟踪产品并验证其真实性。此外,我们的研究结果显示了技术方面的各种挑战,如效率提高、与其他技术的集成、基础设施和缺乏标准。管理层面也存在挑战,比如必须达成可追溯性流程、数据治理、投资和支付意愿、教育以及支持在农场部署技术的协议。在克服了这些公开的挑战之后,区块链技术可以提高可追溯性,并为咖啡供应链中的利益相关者增加价值。
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引用次数: 0
Sentiment analysis for depression detection: A stacking ensemble-based deep learning approach 抑郁检测的情感分析:基于叠加集成的深度学习方法
Pub Date : 2025-07-21 DOI: 10.1016/j.jjimei.2025.100358
Kinza Noor , Mariam Rehman , Maria Anjum , Afzaal Hussain , Rabia Saleem
Depression is one of the most common mental health issues that seriously affect people's quality of life. The World Health Organization reported that depression overwhelms about 300 million people across the globe. Due to the widespread prevalence of this disorder in society, novel and efficient methods must be developed for effective detection and treatment. In the modern era of social media, individuals often reveal their emotional states by providing daily posts on platforms like X (previously Twitter) and Facebook. The information can be utilized as an essential input for determining whether a person has depression based on their writing content. The disclosure of transformer-based deep learning models provides an opportunity to use pre-trained models to successfully capture complex patterns and nuances in the textual data. This study proposes a novel depression detection method through sentiment analysis by developing a Stacking ENSemble-based Deep learning (SENSDeep) model. The proposed model integrates the capabilities of six pre-trained cutting-edge models, including BERT, RoBERTa, AlBERT, DistilBERT, XLNet, and BART, through stacking ensemble to enhance the predicted performance of the proposed model. The SENSDeep model is evaluated by precision, recall, F1-score, and accuracy. In contrast to other models, the SENSDeep model excels with 96.93 % precision, 97.50 % recall, 97.22 % F1-Score, and 97.21 % accuracy. To our knowledge, SENSDeep is the first deep-learning ensemble model that leverages the capabilities of cutting-edge pre-trained transformer models via stacking, specifically for detecting depression from the textual data.
抑郁症是最常见的心理健康问题之一,严重影响人们的生活质量。世界卫生组织报告称,全球约有3亿人患有抑郁症。由于这种疾病在社会上广泛流行,必须开发新颖有效的方法来进行有效的检测和治疗。在现代社交媒体时代,个人经常通过在X(以前的Twitter)和Facebook等平台上发布每日帖子来揭示自己的情绪状态。这些信息可以作为判断一个人是否患有抑郁症的基本输入,基于他们的写作内容。基于转换器的深度学习模型的公开为使用预训练模型成功捕获文本数据中的复杂模式和细微差别提供了机会。本文通过建立基于堆叠集成的深度学习模型,提出了一种基于情感分析的抑郁检测方法。提出的模型集成了六个预先训练的前沿模型的能力,包括BERT、RoBERTa、AlBERT、DistilBERT、XLNet和BART,通过堆叠集成来提高提出的模型的预测性能。通过精度、召回率、f1评分和准确性来评估SENSDeep模型。与其他模型相比,SENSDeep模型的准确率为96.93%,召回率为97.50%,F1-Score为97.22%,准确率为97.21%。据我们所知,SENSDeep是第一个深度学习集成模型,它通过叠加利用了尖端的预训练变压器模型的功能,特别是用于从文本数据中检测凹陷。
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引用次数: 0
Securing the metaverse: Machine learning–based perspectives on risk, trust, and governance 保护元环境:基于机器学习的风险、信任和治理视角
Pub Date : 2025-07-21 DOI: 10.1016/j.jjimei.2025.100356
Krishnashree Achuthan , Sasangan Ramanathan , Raghu Raman
The rapid expansion of the metaverse presents significant cybersecurity and privacy challenges, requiring structured, data-driven analysis. This study applies the ADO-TCM framework and BERTopic modeling to examine drivers of cybersecurity risk, theoretical responses, and interdisciplinary research gaps. Using PRISMA guidelines, 86 peer-reviewed studies were analyzed to identify key antecedents—technological vulnerabilities, user behavior, regulatory fragmentation, economic incentives, and cultural factors—shaping decisions in compliance, deployment, and education. These, in turn, influence outcomes like trust, threat mitigation, and scalability. The review identifies five latent themes: secure identity, privacy, trust, governance, and AI’s role in shaping risk. The study maps diverse theoretical lenses—cognitive, behavioral, strategic, and technological—used to interpret immersive threats and decision-making in metaverse contexts. Contributing a novel, empirically grounded synthesis, this research advances the information management literature and proposes a forward-looking agenda focused on adaptive security, ethical AI, interoperability, regulatory convergence, and intelligent, user-centric architecture for immersive ecosystems.
元宇宙的快速扩张带来了重大的网络安全和隐私挑战,需要结构化的、数据驱动的分析。本研究应用ADO-TCM框架和BERTopic模型来研究网络安全风险的驱动因素、理论响应和跨学科研究差距。使用PRISMA指南,对86项同行评议研究进行了分析,以确定影响合规、部署和教育决策的关键因素——技术漏洞、用户行为、监管碎片化、经济激励和文化因素。这些反过来又会影响信任、威胁缓解和可伸缩性等结果。该报告确定了五个潜在主题:安全身份、隐私、信任、治理和人工智能在塑造风险方面的作用。该研究描绘了不同的理论视角——认知、行为、战略和技术——用于解释虚拟环境中的沉浸式威胁和决策。本研究贡献了一种新颖的、基于经验的综合,推进了信息管理文献,并提出了一个前瞻性议程,重点关注自适应安全、伦理人工智能、互操作性、监管融合以及沉浸式生态系统的智能、以用户为中心的架构。
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引用次数: 0
A Novel Named Entity Recognition approach of Indonesian fake news using part of speech and BERT model on presidential election 基于词性和BERT模型的印尼总统选举假新闻命名实体识别方法
Pub Date : 2025-07-14 DOI: 10.1016/j.jjimei.2025.100354
Puji Winar Cahyo , Ulfi Saidata Aesyi , Widodo Agus Setianto , Tatang Sulaiman
Fake news often spreads rapidly and can mislead readers, which makes it important to approach such information with caution. In text-based information, content extraction can be used to determine the meaning and intent of the message. Therefore, this research aims to develop a novel approach for entity detection in Indonesian-language fake news texts by applying BiLSTM-CRF, BiGRU, and BERT models. The novelty of this study lies in the integration of Part-of-Speech (PoS) tagging before processing words for entity detection. Words tagged as Noun (NN) and Proper Noun (NNP) are transformed into entity labels such as ORG for organizations, PER for people, and LOC for locations. Meanwhile, words labeled as Verb (VB) are converted into the ACT entity to represent actions. Evaluations were conducted by integrating PoS tagging with entity detection using the BiLSTM-CRF model, which achieved an F1-Score of 81.26%. The BiGRU-based model achieved an F1-Score of 79.46%, while the BERT-based model achieved the highest F1-Score of 87.38%. These results demonstrate that the BERT model, when combined with PoS tagging, provides the best performance and can effectively be used to detect entities in fake news. The entity detection process was further applied to identify fake news during the 2024 Indonesian presidential and vice-presidential election period. By counting the number of mentions of each candidate and their running mate labeled as PER entities, it has result the Prabowo Subianto–Gibran Rakabuming Raka pair appeared in 49 fake news articles. This was followed by the Ganjar Pranowo–Mahfud MD pair with 14 fake news articles, and the Anies Baswedan–Muhaimin Iskandar pair with 13 articles. All identified data have been filtered to retain only unique entries.
假新闻往往传播迅速,可能会误导读者,因此谨慎对待这类信息非常重要。在基于文本的信息中,可以使用内容提取来确定消息的含义和意图。因此,本研究旨在通过应用BiLSTM-CRF、BiGRU和BERT模型,开发一种新的印尼语假新闻文本实体检测方法。本研究的新颖之处在于将词性标注整合到处理词之前进行实体检测。标记为名词(NN)和专有名词(NNP)的单词被转换为实体标签,例如ORG代表组织,PER代表人员,LOC代表地点。同时,将标记为动词(VB)的单词转换为表示动作的ACT实体。采用BiLSTM-CRF模型将PoS标注与实体检测相结合进行评价,F1-Score为81.26%。基于bigru的模型F1-Score为79.46%,而基于bert的模型F1-Score最高,为87.38%。这些结果表明,BERT模型与词性标注相结合,可以提供最好的性能,并且可以有效地用于假新闻中的实体检测。实体检测过程进一步应用于2024年印尼总统和副总统选举期间的假新闻识别。通过计算每个候选人及其竞选伙伴被标记为PER实体的提及次数,结果Prabowo Subianto-Gibran Rakabuming Raka夫妇出现在49篇假新闻文章中。紧随其后的是Ganjar Pranowo-Mahfud MD组合,发表了14篇假新闻,Anies Baswedan-Muhaimin Iskandar组合发表了13篇假新闻。所有标识的数据都经过过滤,只保留唯一的条目。
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引用次数: 0
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International Journal of Information Management Data Insights
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