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Integration of Data and Traditional Production Factors: Mechanisms, Paths, and Guarantees Wu Jiang　Yuan Yiming　Miao Jiarui　Zhang Dongying　Lin Zhenyang　Du Le Journal of Information Resources Management    2025, 15 (5): 4-13.   DOI: 10.13365/j.jirm.2025.05.004 Abstract （549）      PDF（pc） （1340KB）（609）       Knowledge map    Save With the accelerated development of the digital economy, the deep integration of data elements with traditional production factors has become a key driver of industrial upgrading, as data elements ultimately realize their value in practical application scenarios. Through literature review and theoretical analysis, this study summarizes the mechanisms by which data elements interact with traditional production factors, including the multiplier effect, the entropy reduction effect of information, and the data network effect. It further explores four synergistic pathways toward all-factor integration, identified from practical application scenarios of such integration. Based on these findings, this study proposes safeguard strategies to promote the coordinated development of data elements and multiple production factors across four application dimensions: land data governance, labor skill transformation, capital allocation, and technological innovation. This study provides theoretical support for unlocking data value and constructing a modernized system of production factors. Related Articles | Metrics | Comments（0）

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Beyond Text-Centrism: The Transformation of Chinese Digital Humanities Driven by Multimodal Technologies Liu Wei　Shan Rongrong　Jin Jiaqin Journal of Information Resources Management    2025, 15 (5): 14-20.   DOI: 10.13365/j.jirm.2025.05.014 Abstract （458）      PDF（pc） （677KB）（345）       Knowledge map    Save Digital humanities research has traditionally centered on textual analysis, yet this "text-centrism" paradigm reveals significant limitations within the Chinese context, including insufficient character set coverage, low OCR accuracy, and the loss of non-textual cultural information, all of which hinder a comprehensive study of China's rich material cultural heritage. The emergence of multimodal technologies offers a transformative pathway for Chinese digital humanities. This paper investigates the predicaments of text-centrism, analyzes solutions enabled by multimodal fusion technologies, and uses DeepSeek’s Janus Pro model as a case study to illustrate the potential of unified multimodal large-scale models in ancient text digitization, intelligent agent development, and cultural heritage preservation. The results show that multimodal technology can reconstruct cultural memory through cross-modal synergy, enhance the public's cultural identity, and provide technical and methodological support for the transformation of Chinese digital humanities. Related Articles | Metrics | Comments（0）

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Exploring the System and Mechanism of National Data Infrastructure for Promoting the Value Release of Data Elements Zhao Yiming　Li Linxin　Luo Lin　Wang Zizhao　Huang Dandi Journal of Information Resources Management    2025, 15 (5): 21-33.   DOI: 10.13365/j.jirm.2025.05.021 Abstract （382）      PDF（pc） （1211KB）（575）       Knowledge map    Save The national data infrastructure serves as the key carrier for the circulation and utilization of data elements throughout their entire life cycle. It plays a strategic role in promoting the aggregation, sharing, security, and efficient use of data resources. From the perspective of value release of data elements, this paper systematically analyzes the functional and institutional requirements for the national data infrastructure. It is proposed that the national data infrastructure should provide support in all stages of data elements value realization, including "creation of use value—mining of application value—release of multiple values". Furthermore, this paper constructs a three-dimensional organizational system of "region—industry—enterprise" and an operational mechanism that includes "one core, two pillars, and two safeguards." This builds a comprehensive system for the construction and operation of national data infrastructure, covering the entire data element chain across platforms and hierarchical levels. Additionally, safeguard measures for the innovation of the construction and operation system mechanism are proposed from the dimensions of technological innovation, institutional construction, and ecological collaboration. This study provides theoretical basis and practical reference for promoting the high-quality development of national data infrastructure in China and accelerating the release of data element value. Related Articles | Metrics | Comments（0）

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Influencing Factors of Discontinuous Usage Behavior of Mobile Short-Form Video Users: A Systematic Literature Review Dai Bao　Zheng Yiqing　Yang Liying Journal of Information Resources Management    2025, 15 (5): 131-146.   DOI: 10.13365/j.jirm.2025.05.131 Abstract （357）      PDF（pc） （1486KB）（646）       Knowledge map    Save This paper aims to explore the factors influencing the discontinuous usage behavior of mobile short-form video users, with the purpose of providing theoretical guidance for operators to develop effective user retention strategies and offering insights for deepening related studies. Based on the “Theory-Context-Methodology” (TCM) framework, the paper first systematically reviewed the research status of mobile short-form video users' discontinuous usage behavior, examining theoretical foundations, research contexts, and methodological approaches both domestically and internationally. Subsequently, it synthesized the influencing factors and mechanisms underlying this behavior by integrating the dual-factor (enabling-inhibiting) perspective, the information ecology perspective, and the S-O-R model.The present study reveals that the discontinuous usage behavior of mobile short-form video users is significantly influenced by enabling factors such as privacy concerns, information overload, system feature overload, and upward social comparison, as well as inhibiting factors including switching costs, information timeliness, platform usefulness, and social influence. Related Articles | Metrics | Comments（0）

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“Objectives-Instruments-Subjects” Triangular Framework: A Quantitative Analysis of China’s Data Asset Management Policies Zhang Wei　Ye Shiqi Journal of Information Resources Management    2025, 15 (5): 66-81.   DOI: 10.13365/j.jirm.2025.05.066 Abstract （339）      PDF（pc） （4955KB）（214）       Knowledge map    Save The policy text on data assets plays a crucial role in the forward-looking, guiding, and coordinated management of data assets. It is crucial to scientifically recognize its value orientation, specific measures, and subject layout to promote the establishment and improvement of the data asset policy system. This study has built a three-dimensional analytical framework of “policy objectives-policy instruments-policy subjects” and conducted multi-dimensional quantitative analysis on 36 policy texts based on this framework. It has sorted out that mechanism innovation, risk prevention and control, value exploitation, ecological cultivation are the four major pathways of the current policy system. The three major real problems in the current data asset policy system are imbalanced policy objectives, imbalanced policy instrument structures, and insufficient motivation of policy subjects. In the future, it is necessary to break through the development dilemma by strengthening policy goal coordination, optimizing policy instrument structures, and giving full play to the role of policy subjects. This will promote the comprehensive management and compliance, standardization, and value-added management of data assets. Related Articles | Metrics | Comments（0）

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Technology Opportunities Identification and Evaluation Based on Policy-Technology Topic Association：A Case Study of the New Energy Vehicle Industry Zhang Zihan　Li Yang　Wu Keye Journal of Information Resources Management    2025, 15 (5): 82-98.   DOI: 10.13365/j.jirm.2025.05.082 Abstract （305）      PDF（pc） （7117KB）（222）       Knowledge map    Save Previous studies on technology opportunity identification are mostly limited to the perspective of technology itself, pay insufficient attention to policy documents that directly reflect the major strategic demands of a country. In view of this, this paper proposes a technology opportunity identification method based on policy-technology topic association, aiming to explore the potential gaps between policy demands and technological development. Specifically, this paper uses policy documents to represent policy demands and patent technologies to represent the degree of technological development. The identification of technology opportunities is divided into three modules: topic extraction of policies and technologies, topic association, and identification and evaluation of policy-technology integration opportunities. Firstly, this paper identifies policy and technology topics by constructing a LDA topic model. Then, word embedding and hierarchical clustering methods are used to establish the mapping relationship between policy and technology. This paper locates technology opportunity by constructing identification quadrant according to the identification rules. Finally, This study takes the field of new energy vehicles as an example to verify the effectiveness of research framework, which provides information support for country and research institutions to accelerate the layout in this field. Related Articles | Metrics | Comments（0）

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Construction of an Evaluation Indicator System for High-Quality Datasets in the Data Element Market Lin Zhenyang 　Wu Jiang　Hu Xin　Wang Jingxuan　Yuan Yiming　Du Le Journal of Information Resources Management    2025, 15 (6): 52-66.   DOI: 10.13365/j.jirm.2025.06.052 Abstract （289）      PDF（pc） （1252KB）（945）       Knowledge map    Save This study aims to construct a scientific data quality evaluation indicator system as a fundamental basis for promoting the market-oriented allocation of data elements. It provides a quantitative benchmark for the transformation of data resources into assets and capital, thereby supporting the development of a value circulation mechanism in the data element market. Based on grounded theory, this study systematically analyzes policy documents, technical standards, and expert interview materials to build a multi-level evaluation indicator system comprising four main dimensions—compliance characteristics, scale-related attributes, content-specific properties, and value-oriented features—with 12 first-level indicators and 32 second-level indicators. This study further adopts the Analytic Hierarchy Process (AHP) and expert consultation methods to determine indicator weights and develop a practical, operable comprehensive evaluation model. Empirical validation through the selection of high-quality datasets in Hubei Province demonstrates the model’s effectiveness and practical applicability. The findings provide theoretical support and practical references for data asset valuation, improving data circulation efficiency, and optimizing value transformation pathways. Related Articles | Metrics | Comments（0）

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Bidirectional Influence of Media on the Emergence and Inhibition of Cyberbullying in Group Polarization Effect Ma Xiaoyue　Zhang Liubin Journal of Information Resources Management    2026, 16 (1): 23-36.   DOI: 10.13365/j.jirm.2026.01.023 Abstract （284）      PDF（pc） （2187KB）（526）       Knowledge map    Save The phenomenon of cyberbullying triggered by the group polarisation effect is endless and seriously disrupts social order, but the transformative relationship and internal structure between the group polarisation effect and cyberbullying are vague, and there is a theoretical blind spot. The study employed a combination of social support theory and qualitative comparative analysis to perform configuration analysis, as well as ordered logistic regression for supplementary testing. The study found that the emotional support framework is the trigger for cyberbullying and runs through most paths. The information support framework can induce negative emotions and is a prerequisite for cyberbullying. The tool support framework plays a more auxiliary role in cyberbullying by helping to construct the media environment. Finally, the evaluation support framework has a restraining effect on cyberbullying, though its effectiveness is affected by emotional fluctuations. Official intervention significantly impacts cases involving intense emotional turmoil, while artificial intelligence intervention and official media agenda-setting can suppress cyberbullying by shifting attention and defusing polarized positions. This study clarifies the process from the emergence of group polarisation tendencies to the development of cyberbullying, constructs a mechanism for integrating cyberbullying group dynamics, and provides a theoretical basis for cyberspace governance. Related Articles | Metrics | Comments（0）

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Topic Mining and Spatial Effect of China’s Digital Economy Policy Texts Xu Huichao　Zhao Yanyun Journal of Information Resources Management    2025, 15 (5): 51-65.   DOI: 10.13365/j.jirm.2025.05.051 Abstract （284）      PDF（pc） （4583KB）（549）       Knowledge map    Save The digital economy policy is one of the manifestations of the government's support for the development of the digital economy, and the analysis of policy characteristics is helpful to clarify the implementation effect of the policy. Based on the characteristics of the digital economy and from the perspective of the digital economy industry chain, this study uses 468 comprehensive digital economy policies in China from 2017 to 2022, combined with text analysis methods, network analysis methods and spatial econometric models. The results show that China's digital economy policies are divided into eight categories: talent training, enterprise and park development, service platform, technology development, industrial integration development, declaration and evaluation, management and planning, and rewards and subsidies. Overall, the digital economy policies have significantly promoted the development of the digital economy in both the region and neighboring regions. Policies related to talent training and technology development have shown significant positive externalities, and policies on enterprise and park development, service platforms, technology development, incentives and subsidies, and management and planning all show obvious digital economy promotion effects. The number of digital economy policies has a long-term impact on the development of the digital economy, while the intensity of digital economy policies has a significant effect in the short term. Related Articles | Metrics | Comments（0）

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Research on the Development of Information Resources Management Discipline from the Perspective of NSFC Funding Hu Jiming　Yang Yun Journal of Information Resources Management    2025, 15 (5): 147-161.   DOI: 10.13365/j.jirm.2025.05.147 Abstract （283）      PDF（pc） （12506KB）（178）       Knowledge map    Save From the perspective of National Natural Science Foundation projects, this study delves into the theme and direction synthesis of research in the field of Information Resources Management, aiming to grasp the trends in this discipline as it undergoes transformation. A framework for analysing the developmental trends of the discipline has been constructed, integrating theme mining and deep learning models. Based on the NSFC's G0414 project data from the past five years, this study conducts visual analyses of project theme identification, differences between application and funding themes, thematic association structures, and their evolution over time.The funding ratio for the Information Resource Management discipline remains stable and at a high level, primarily concentrated among a few top-ranking universities. A limited number of thematic areas have received final funding, including information demand, multimodal computing, knowledge discovery, intelligent empowerment, large models, and risk management. These themes exhibit varying degrees of interconnection and influence, characterized by ongoing evolutionary traits. In the realm of natural science research, the overall development of the Information Resource Management discipline is promising, with stable funding levels focusing on specific research directions. However, there is a significant discrepancy between project applications and funding outcomes. Related Articles | Metrics | Comments（0）

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Research on the Intervention Mechanism of Public Opinion Information Dissemination in Deepfake Events Based on AIGC Yang Yangyang Journal of Information Resources Management    2026, 16 (1): 37-49.   DOI: 10.13365/j.jirm.2026.01.037 Abstract （273）      PDF（pc） （1312KB）（373）       Knowledge map    Save Based on trust theory and the characteristics of deepfake event public opinion information generated by artificial intelligence, this paper analyzes the impact mechanism of intervention strategies on system trust, interpersonal trust, emotional trust, and cognitive trust from three types of communication intervention strategies: information strategy, responsibility strategy, and behavior strategy, with the research framework of "intervention strategy-perceived trust-post-intervention trust". The study finds that information strategy indirectly and positively affects post-intervention trust through interpersonal trust and cognitive trust, responsibility strategy indirectly and positively affects post-intervention trust through system trust and emotional trust, and behavioral strategy indirectly and positively affects post-intervention trust through system trust and interpersonal trust, while control variables (gender, age, and education) have no significant impact on post-intervention trust. The influence of intervention strategies on perceived trust does not vary significantly across different gender types. Related Articles | Metrics | Comments（0）

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Exploring the Influence Mechanism of Social Media Users’ Algorithm Awareness on Privacy Risk Coping Behaviors: A Perspective from Algorithm Abuse Meng Xi　Li Qingshuang　Guo Yajun Journal of Information Resources Management    2025, 15 (5): 99-115.   DOI: 10.13365/j.jirm.2025.05.099 Abstract （271）      PDF（pc） （2169KB）（392）       Knowledge map    Save Based on the APCO (Antecedents-Privacy Concerns-Outcomes) model framework, this study adopts a mixed-method approach combining qualitative and quantitative research to examine the impact of users’ algorithm awareness and privacy concerns on their privacy risk coping behaviors, from the perspective of algorithm abuse. In the qualitative study, in-depth interviews with 24 social media users were conducted and analyzed with grounded theory, aiming to identify key dimensions of privacy concerns under the lens of algorithm abuse. In the quantitative study, survey data from 513 users were empirically analyzed using structural equation modeling to examine the direct effect of algorithm awareness on privacy risk coping behaviors and to further explore the mediating roles of five identified privacy concern factors. Results from the qualitative analysis reveal five key privacy concern dimensions: perceived privacy intrusion, perceived algorithm surveillance, perceived algorithm bias, perceived algorithmic decision-making risk, and perceived data permanence risk. Empirical results show that algorithm awareness has a significant positive impact on privacy risk coping behaviors. Moreover, perceived privacy intrusion, algorithm bias, algorithmic decision-making risk, and data permanence risk partially mediate this relationship, while the mediating role of perceived algorithm surveillance is not significant. These findings provide practical evidence to support the governance of algorithm abuse risks and the management of user privacy risks in China. Related Articles | Metrics | Comments（0）

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Identifying Knowledge Heuristic Intervention and Enhancement Patterns for Algorithmic Literacy in Personalized Recommendation Contexts Liu Jing　Bai Fangrui　Wu Dan Journal of Information Resources Management    2025, 15 (5): 116-130.   DOI: 10.13365/j.jirm.2025.05.116 Abstract （268）      PDF（pc） （9235KB）（248）       Knowledge map    Save Personalized recommendations significantly influence daily life and represent a key shift in information control toward algorithms. This change necessitates new skills for individuals to perceive, understand, and utilize these algorithms effectively, highlighting an urgent need for research on algorithmic literacy within the context of personalized recommendations. This study concentrated on personalized recommendation contexts, developing a knowledge heuristic intervention to enhance algorithmic literacy. A 4-week longitudinal user experiment involving 30 participants was conducted, with statistical comparisons and analyses of changes in algorithmic literacy, as well as the identification of enhancement patterns through cluster analysis. Before and after the experiment, users showed significant improvements in different dimensions of algorithmic literacy, confirming the effectiveness of the knowledge heuristic intervention. Cluster analysis identified three enhancement patterns of algorithmic literacy: gradual improvement with weak foundations pattern, Knowledge-Skill enhancement with weak motivation pattern, and awareness enhancement with strong motivation pattern. This study further analyzed user characteristics associated with each pattern and proposed tailored knowledge heuristic strategies for enhancing algorithmic literacy based on these characteristics. Related Articles | Metrics | Comments（0）

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Cyberbullying Dissemination in Intelligent Society under Algorithmic Dual-roles: Trends, Motivations and Governance Paths Chen Ye　Xu Hao　Cheng Qingxuan Journal of Information Resources Management    2026, 16 (1): 10-22.   DOI: 10.13365/j.jirm.2026.01.010 Abstract （267）      PDF（pc） （2600KB）（282）       Knowledge map    Save The intelligent society amplifies the risks of cyberbullying dissemination, rendering traditional governance mechanisms ineffective. As a core technological component, algorithms serve dual-roles: both as enablers of cyberbullying and as potential restrainers. This paper investigates this dual-roles through literature review and theoretical analysis, systematically examining the dissemination trends of cyberbullying characterized by algorithmic empowerment and psychological dynamics. We dissect the operational mechanisms through which algorithms exacerbate cyberbullying via content fabrication, emotional manipulation, and conflict amplification, while simultaneously analyzing their counteractive potential through content suppression, environmental purification, and behavioral guidance. Building upon this dual-roles analysis, we propose a collaborative governance framework involving multi-stakeholder participation from governments, platforms, algorithm designers, and users. This paper contributes theoretical innovation and practical guidance for addressing cyberbullying governance challenges in intelligent society, offering insights into the synergistic optimization of technological governance and social regulation mechanisms. Related Articles | Metrics | Comments（0）

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A Typical Action Integrating Intelligence and Data: Data Sensemaking in User-Generated Intelligent Search Engine Interactions Peng Siyuan　Wu Siying　Ling Shang　Li Qiao　Wang Ping Journal of Information Resources Management    2025, 15 (5): 34-50.   DOI: 10.13365/j.jirm.2025.05.034 Abstract （250）      PDF（pc） （3569KB）（287）       Knowledge map    Save Generative intelligent search engines, which integrate generative artificial intelligence technology and retrieval techniques, have the potential to support researchers in overcoming challenges during data sensemaking, but they also come with certain risks. Drawing upon sense-making theory, information behavior model, information search process model, and information search behavior model, this study preliminarily proposes the Data Search As Data sensemaking(DS-DSM) theoretical model. To explore this model, this study conducts a user experiment on the Bing Copilot platform to understand how researchers construct the meaning of data through interaction with generative intelligent search engines. The findings indicate that in the interaction scenario of generative intelligent search engines, the essence of researchers’ data search is data sensemaking. This study also identifies the stages of this process and reveals how, at each stage, researchers cognitively, affectively, and behaviorally engage with generative intelligent search engines as a bridge to overcome gaps in the data sensemaking process to complete data-centered tasks. In data-related task situations, researchers’ sensemaking begins at the formulation stage. In contrast, in research-related task situations, some researchers first go through initiation, selection, and exploration stages without clear goals. In the formulation stage, the types of gaps researchers face and the bridges they use are the most diverse, showing the most complex behavioral, cognitive, and affective responses. Based on these findings, this study proposes practical strategies for optimizing the design of generative intelligent search engines and conducting user literacy education. Related Articles | Metrics | Comments（0）

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Research on the Overall Framework and Implementation Path of Data Circulation and Utilization in China: A Perspective Based on Data Infrastructure Construction Zhao Zheng　An Xiaomi　Guo Mingjun Journal of Information Resources Management    2025, 15 (6): 67-81.   DOI: 10.13365/j.jirm.2025.06.067 Abstract （241）      PDF（pc） （4974KB）（184）       Knowledge map    Save Accelerating the circulation and utilization of data to fully unleash its value relies on robust data infrastructure. This paper integrates domestic and international research foundations and current practices in data circulation and utilization, deeply analyzing the new demands that data elements place on infrastructure. It summarizes the latest trends in global infrastructure development, and based on this, addresses practical challenges such as trustworthy data supply, efficient processing, active circulation, and practical application. This paper proposes a comprehensive framework for data circulation and utilization, consisting of "one computational support base, two types of resource-coordinated scheduling, a three-tier efficient circulation system, and four-dimensional innovative integration demonstrations." It then provides specific recommendations for implementation at different stages, focusing on strengthening foundational support, optimizing circulation systems, and improving application ecosystems. These insights aim to offer valuable references for accelerating data infrastructure development and promoting efficient data circulation and utilization in the new era. Related Articles | Metrics | Comments（0）

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China’s Internet History Through a Multi-Narrative Lens：A Book Review of A Brief History of the Internet in China Li Gang Journal of Information Resources Management    2025, 15 (5): 162-164.   DOI: 10.13365/j.jirm.2025.05.162 Abstract （221）      PDF（pc） （555KB）（182）       Knowledge map    Save The development of the internet in China has spanned three decades. The historic leap from a large internet nation to a strong internet power urgently requires in-depth summarization and interpretation by academia. Against this backdrop, the publication of the book A Brief History of the Internet in China carries significant contemporary significance and academic value. By reconstructing historical contexts, focusing on key actors, and integrating diverse historical materials, the book breaks away from the previously dominant government-centered linear narrative, vividly presenting the tapestry of China's internet development woven collectively by the government, the market, academia, and communities. Moreover, its interwoven thematic narratives not only provide a systematic historical annotation for the development of the internet in China but also offer a theoretical reference for the evolution of disciplines such as information resource management, highlighting China's transition from a field of technological application to a site of theoretical construction. Related Articles | Metrics | Comments（0）

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Value Co-creation in the Public Data Authorization and Operation Ecosystem: A System Dynamics Approach Chen Mei　Ding Fangxin Journal of Information Resources Management    2025, 15 (6): 82-95.   DOI: 10.13365/j.jirm.2025.06.082 Abstract （211）      PDF（pc） （6423KB）（174）       Knowledge map    Save This study aims to explore the value co-creation mechanisms within the public data authorization and operation ecosystem. Drawing on information ecosystem theory and value co-creation theory, this study applied grounded theory to analyze 13 public data authorization and operation policies, identifying the key components of the public data authorization and operation ecosystem: data entities, data, and data environment. We then constructed a system dynamics model to simulate the evolution of value co-creation and data outcome application over a 12-month period. The simulation results show that after an initial slow growth phase, both indicators increased significantly after two months. Sensitivity analysis further revealed the positive impact of six key factors on value co-creation: diversity of stakeholders, data governance capabilities, user demand, technological environment, policy environment, and market environment, with the data entity subsystem exerting a particularly significant impact. Related Articles | Metrics | Comments（0）

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Factors Influencing the Communication Effect of Doctor-Produced Popular Health Science Short Videos Yong Yuhao　Li Xinyue　 Zhao Mengyuan　Ying Jun Journal of Information Resources Management    2026, 16 (2): 69-81.   DOI: 10.13365/j.jirm.2026.02.069 Abstract （208）      PDF（pc） （1536KB）（118）       Knowledge map    Save Focusing on doctors as the core communicators, this study develops a theoretical model based on theories of social presence, cognitive load, and media richness to investigate how multimodal features influence the dissemination effectiveness of health science short videos. Using a dataset of 2,683 health science short videos published by doctors on Douyin, we conducted regression analysis and robustness tests, integrating methods such as audio analysis, text mining, and image recognition. The study examines how video content formats and doctors' self-presentation characteristics affect dissemination effectiveness through mediating factors, including cue multiplicity, interactivity, authenticity, and emotional resonance. Results indicate that the inclusion of case analyses, evidence-based support, and highly cohesive language significantly enhances dissemination effectiveness. In contrast, explicitly prompting user interactions such as comments or likes, as well as emphasizing the doctor's professional identity or clinical setting, significantly reduces it. From the perspective of doctors' professionalism, this study clarifies the multimodal influencing mechanisms underlying the dissemination effectiveness of doctor-led health science short videos. It also enriches research perspectives on science communication via short videos, and offers practical insights for professional institutions and individuals engaged in health science popularization. Related Articles | Metrics | Comments（0）

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From Emotional Fields to Topic Engagement: The Dual Mechanisms of Social Bots in Disseminating Misinformation Zhang Shiying　Ke Qing Journal of Information Resources Management    2026, 16 (1): 50-62.   DOI: 10.13365/j.jirm.2026.01.050 Abstract （204）      PDF（pc） （10577KB）（113）       Knowledge map    Save This study investigates the intrinsic mechanisms of social bots in disseminating misinformation from the dual perspectives of emotional fields and content topics. Drawing on a large-scale dataset of misinformation on Weibo, and employing methods including machine learning, zero-shot sentiment classification, BERTopic, and negative binomial regression, this research explores how social bots participate in misinformation propagation through dual pathways: group emotional fields and topic engagement heat. Key findings include: 1) social bots tend to engage more actively with public issues that have high social influence or are prone to controversy; 2) the involvement of bots significantly increases emotional entropy, thereby enhancing the diversity of collective emotional expression; 3) emotional polarization is more likely to emerge as a natural outcome of human user interaction rather than being directly triggered by bots; 4) both emotional entropy and emotional polarization exhibit dual effects and topic heterogeneity in influencing the virality of misinformation. This study provides a dual-dimensional analytical framework for understanding misinformation dissemination mechanisms in the era of human-bot symbiosis. Related Articles | Metrics | Comments（0）

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Research on Intelligent Intelligence Service Model for Industrial Technology Innovation: From the Perspective of Context-Driven Innovation Wei Jinyu　Mao Jin　Li Gang　Quan Zhibang Journal of Information Resources Management    2025, 15 (6): 5-19.   DOI: 10.13365/j.jirm.2025.06.005 Abstract （204）      PDF（pc） （9160KB）（544）       Knowledge map    Save From the perspective of context-driven innovation, this paper constructs a new intelligent intelligence service model for Industrial Technological Innovation (ITI) from two aspects: theoretical framework and applied implementation. The service theoretical framework proposes a construction pathway for the intelligence service model based on the 'demand mining-scenario depiction-service response' logical chain. The applied implementation guided by the theoretical framework, focuses on eight task scenarios, such as technology insights, R&D efficiency, and so on. Then we provide a detailed analysis of the implementation path and function of the ITI framework from service content and service methods, and intelligent intelligence service system architecture is proposed. This paper aims to deeply embed intelligent intelligence workflows into ITI scenarios, and promoting intelligence services towards context-based and intelligent approaches. It provides a new theoretical framework and implementation pathway for the development and practical application of science and technology intelligence in the intelligent era. Related Articles | Metrics | Comments（0）

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Construction of a Knowledge Graph for "Huoji Dang" Driven by Large Language Models Sui An　Tong Yongsheng　Gao Ning Journal of Information Resources Management    2026, 16 (1): 116-130.   DOI: 10.13365/j.jirm.2026.01.116 Abstract （198）      PDF（pc） （12578KB）（170）       Knowledge map    Save By constructing a knowledge graph of the Qing Yongzheng Imperial Workshop archives, this study systematically organizes the artefact-related information recorded in the Huoji Dang, thereby overcoming the fragmented limitations of traditional research and providing new perspectives and methodological support for the study of imperial artefacts in the Qing dynasty. The research employs large language models (LLMs) and prompt engineering techniques. After iteratively optimizing prompts and incorporating manual verification, the extracted data are stored in a Neo4j graph database, enabling the automatic transformation of unstructured data in the Huoji Dang into a structured knowledge graph. Results demonstrate that DeepSeek-V3 outperforms other LLMs across all evaluation metrics, with clear overall advantages. In sum, this method effectively and accurately extracts entities, attributes, and relationships, and the resulting knowledge graph clearly illustrates the organizational structure, production processes, and imperial aesthetic preferences embedded in the archives. Moreover, the proposed “LLM + prompt engineering” approach demonstrates strong transferability in addressing the automatic extraction of premodern texts, offering a valuable reference for similar studies. It realizes the structuring and visualization of textual information, thereby providing a systematic knowledge-sharing platform for research on Qing imperial artefacts. Related Articles | Metrics | Comments（0）

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Drivers and Barriers in Science-to-Technology Transfer: An Empirical Study Based on Exponential Random Graph Model Ma Ming　Mao Jin　Zou Dangyi　Li Gang Journal of Information Resources Management    2025, 15 (6): 20-36.   DOI: 10.13365/j.jirm.2025.06.020 Abstract （191）      PDF（pc） （5208KB）（319）       Knowledge map    Save Investigating the mechanism of knowledge flow from science to technology helps understand how scientific progress drives technological innovation. Thus, this paper first constructed a "science-technology" knowledge transfer network composed of keyword citation. Then, using exponential random graph models, we integrated knowledge attributes with the knowledge transfer process in a modeling approach that simultaneously considered endogenous network structures. Finally, we conducted an empirical analysis based on scientific papers and patent data in the gene editing field from 1990 to 2018. We find that the high economic value of scientific and technological knowledge inhibits knowledge transfer, as rational actors tend to engage in exploitative innovation based on existing high-value knowledge. However, the convergence of economic value facilitates the transfer process by helping to reduce transfer barriers through moderate cognitive distance. The academic value of knowledge contributes to advancing the knowledge transfer process, but this effect is not statistically significant. Under the influence of homogeneity effects, knowledge novelty and geographic proximity have a positive impact on the formation of knowledge transfer relationships from science to technology. Meanwhile, comparison with random networks demonstrates that the citation behavior of technological knowledge toward scientific knowledge may not be influenced by semantic proximity or knowledge potential. These results demonstrate consistency across knowledge network simulation models in different time periods. Related Articles | Metrics | Comments（0）

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Early Identification of Breakthrough Papers from the Perspective of Innovation Scenario: An Application in the Biomedical Field Zhang Shuqian　Huang Shan　Mao Jin　Li Gang Journal of Information Resources Management    2025, 15 (6): 37-51.   DOI: 10.13365/j.jirm.2025.06.037 Abstract （186）      PDF（pc） （6249KB）（176）       Knowledge map    Save The timely and accurate identification of breakthrough scientific literature is of crucial strategic significance for the efficient allocation of scientific research resources, seizing the initiative in technological development, and enhancing national core competitiveness. However, existing identification methods have limitations such as single-dimensional indicators and insufficient recognition efficiency. From the contextual perspective of knowledge innovation, this study constructs a characteristic system that distinguishes breakthrough papers from ordinary papers from three aspects: knowledge foundation, research team, and academic attention, and proposes a machine learning-based early identification method for breakthrough papers. Experiments in the field of biomedicine show that the F1-score of the model in this study reaches 0.838, which verifies the effectiveness of the method. Among the factors, early-stage impact, number of references, and Price Index have the most significant impact on the model's recognition results. This study enriches and expands the theoretical framework and methodological system for breakthrough paper identification from the novel perspective of innovation context. Related Articles | Metrics | Comments（0）

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How Research Team Knowledge Background Composition Affects Knowledge Innovation: A Comparative Interdisciplinary Analysis Li Xinzhe　Lu Xiao Journal of Information Resources Management    2025, 15 (6): 143-156.   DOI: 10.13365/j.jirm.2025.06.143 Abstract （185）      PDF（pc） （9110KB）（195）       Knowledge map    Save This study examines how research team knowledge background composition affects the knowledge innovation across eight major scientific fields by analyzing 32.63 million co-authored papers from the Crossref dataset spanning 1945-2023. Using Monte Carlo simulation and natural language processing, this study developed metrics to characterize team knowledge background composition, including internal knowledge similarity among team members and external knowledge similarity among teams. It was found that (1) Different knowledge background compositions exhibit significant differences in their capacity to generate knowledge innovation, with specific team knowledge background compositions demonstrating markedly superior innovation capabilities compared to others, indicating that team knowledge background composition has a decisive impact on knowledge innovation; (2) Each field has its own optimal team knowledge background composition: physics favors diverse teams with differentiated research directions, while social sciences perform better with specialized team compositions, suggesting that innovation policies should be tailored to specific disciplines; (3) Optimal team knowledge background compositions consistently represent only a minority across all fields, indicating structural misalignment in current research systems; meanwhile, despite exponential growth in publication volume, high-level knowledge innovation output remains constant across fields, revealing a "carrying capacity" phenomenon that highlights the disconnect between research scale expansion and knowledge innovation. These findings have important implications for scientific practice and policy development. Related Articles | Metrics | Comments（0）

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Information Behaviors and Influencing Factors in the Public’s Acquisition of Government-Disclosed Information during Public Emergencies Zhang Tairui　Li Yuelin　Zhang Jianwei Journal of Information Resources Management    2025, 15 (6): 96-111.   DOI: 10.13365/j.jirm.2025.06.096 Abstract （183）      PDF（pc） （2744KB）（156）       Knowledge map    Save This study aims to clarify the Public’s Acquisition of Government-Disclosed Information during Public Emergencies, explore the process of public information acquisition and its influencing factors, and corstruct a theoretical framework for information acquisition. The research employed semi-structured in-depth interviews and quqlitative data analysis to reveal the process of public information acquisition, and futher analyzed the characteristics and influcing factors in the process of public information acquisition through questionnaire surveys and logistic regression. The results indicate that three factors motivate the public to acquire information during emergency, such as information tracing, information comparison, and information supplementation, leading to acquisition behaviors migrate from passive information acquisition to active seeking. Additionally, the public’s preferences for information sources, information channels and information forms are influenced by various factors. This study helps the government to understand the process of public information acquisition during emergencies, and lays down a theoretical and practical foundation for improving the quality of information disclosure. Related Articles | Metrics | Comments（0）

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A Knowledge Recombination Prediction Framework Based on Heterogeneous Graph Neural Networks Ren Anxing　Yang Guancan　Xing Jiaxin　Zhang Zihe Journal of Information Resources Management    2025, 15 (6): 129-142.   DOI: 10.13365/j.jirm.2025.06.129 Abstract （183）      PDF（pc） （7595KB）（165）       Knowledge map    Save Knowledge recombination is pivotal for fostering innovation and interdisciplinary integration. Existing studies typically rely on homogenous knowledge networks for its early prediction, which fail to capture the intricate relationships between knowledge units and their associated entities, thereby constraining predictive performance. To address this limitation, this paper proposes a knowledge-recombination prediction framework based on heterogeneous graph neural networks. The framework integrates multiple heterogeneous entities and relations closely related to knowledge units, constructs an enriched heterogeneous knowledge network through diverse connection strategies, and employs a relation-aware graph convolutional network to predict potential recombination links. Empirical experiments in the cancer immunotherapy domain demonstrate that the proposed framework markedly outperforms traditional homogenous-network baselines, with the F1 score rising from 0.706 to 0.889. The results also confirm that connection strategies for heterogeneous nodes significantly influence predictive performance, underscoring the importance of heterogenous network design in knowledge-recombination prediction. Related Articles | Metrics | Comments（0）

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An Exploration of the Core Categories and Knowledge System of Information Resources Management Discipline in China Ma Feicheng　Liu Zhenghao　Chen Shuaipu Journal of Information Resources Management    2026, 16 (3): 5-20.   DOI: 10.13365/j.jirm.2026.03.005 Abstract （181）      PDF（pc） （6209KB）（145）       Knowledge map    Save Since the renaming of Information Resources Management as a first-level discipline, the reconstruction of the core categories and knowledge system of the discipline has become a core issue in discipline development. This study, grounded in the contemporary context of constructing an independent knowledge system for Chinese disciplines, systematically discusses three core questions: the origin of the discipline, the meaning of its content, and the structure of its system. First, it extracts three core characteristics, “sequential annotation, in-depth revelation, and value transformation” from the historical logic of the discipline's evolution, establishing its essential core. Second, through comparative analysis and commonality extraction of the basic categories of the second-level discipline group, it constructs a core category system imbued with the philosophical wisdom of naming Information Resources Management as a discipline. Then, using these three characteristics as a logical starting point, it builds a theoretical framework for the knowledge system based on these core categories, positioning disciplinary knowledge along three dimensions: management object, management process, and management value, and presenting the abstract depth of knowledge through three levels: theory, method, and practice. This framework integrates and deepens the core categories, providing a systematic analytical tool for the construction of the knowledge system, research positioning, and academic evaluation of Information Resources Management. Finally, this article takes the research of Information Resources Management discipline in response to the questions of the people, China, the world, and the times as a clue, and clarifies the value orientation and future direction of the independent knowledge system. Related Articles | Metrics | Comments（0）

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International Development of Marine Data Resource Exploitation System and China's Response Mei Ao　Zhang Jiayi Journal of Information Resources Management    2026, 16 (1): 4-9.   DOI: 10.13365/j.jirm.2026.01.004 Abstract （176）      PDF（pc） （683KB）（162）       Knowledge map    Save The development and utilization of marine data resources is an important part of digital economic development. Under the framework of the United Nations Convention on the Law of the Sea, all countries in the world carry out in-depth cooperation and promote the development and utilization of international marine data resources. China's marine data governance has ushered in new strategic opportunities and challenges, and going to deepen marine cooperation among countries under the guidance of the concept of "ocean community of shared future". Through sorting out the current situation of the development and utilization of marine data, there are deficiencies in both the development and utilization phases, and further explanations of the corresponding strategies are made based on the goal of building a marine power, and feasible suggestions for development and utilization of marine data in China and the world are put forward. Related Articles | Metrics | Comments（0）

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Adaptation to Local Conditions: Local Differentiated Paths and Configuration Analysis of Public Data Authorization and Operation Song Xinran　Li Ju　Lin Tong Journal of Information Resources Management    2026, 16 (1): 78-88.   DOI: 10.13365/j.jirm.2026.01.078 Abstract （174）      PDF（pc） （1094KB）（101）       Knowledge map    Save Public data authorization operation is a data development and utilization approach with Chinese characteristics. Analyzing the differentiated practical paths in various regions is of great significance for exploring and forming replicable, easily-promoted and sustainable data circulation and utilization operation models. Based on the information ecology theory, this paper conducts a configuration analysis of 25 cities that have carried out public data authorization operations using csQCA. The research finds that data quality and the official information field are necessary conditions for good public data authorization operation effects. Government-led early practice type, government-coordinated data empowerment type and market-driven ecological empowerment type are three paths with good practical effects, while system-to-be-built type and data system shortcoming type are two paths with poor practical effects. Data has dual attributes of private goods and public goods. Market-oriented and centralized systems are needed to guarantee different stages of data operation. Each region should choose an appropriate authorization operation path in line with its local conditions based on the data ecosystem. Related Articles | Metrics | Comments（0）

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Intelligent Innovation Evaluation Engineering： A New Paradigm for Scientific Research Innovation Evaluation in the Digital Intelligence Era Lu Wei　Huang Yong Journal of Information Resources Management    2026, 16 (2): 4-10.   DOI: 10.13365/j.jirm.2026.02.004 Abstract （172）      PDF（pc） （2134KB）（144）       Knowledge map    Save In the context of the digital intelligence era, the core challenge of scientific research innovation evaluation has evolved from partial improvements in metrics and methods to a structural dilemma where evaluation systems fail to operate stably and cannot be embedded in scientific research governance practices. This study proposes a new paradigm termed "Intelligent Innovation Evaluation Engineering", which reconstructs the organizational logic of evaluation activities from a systems engineering perspective. Supported by explainable artificial intelligence, grounded in open sharing as an institutional foundation, and realized through modular engineering integration, this paradigm comprises five synergistic modules: data integration and shared services, dynamic management of evaluation indicators, modeling of scientific research management processes, mapping mechanisms between indicators and processes, and intelligent tool construction. The objective is to upgrade scientific research innovation evaluation from fragmented and closed tool-based research to an integrated, open, and sustainable socio-technical system, thereby enhancing the reproducibility, interpretability, and embeddability of evaluation systems, facilitating deep integration of evaluation into the entire process of scientific research governance, and providing a systematic solution for modernization of scientific research governance in the digital intelligence era. Related Articles | Metrics | Comments（0）

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Research on the Value of Data Assets Based on Scenario-Based Dynamic Assessment Xia Wenlei　Wei Zimeng　Yu Hui　Cui Wei Journal of Information Resources Management    2026, 16 (2): 25-39.   DOI: 10.13365/j.jirm.2026.02.025 Abstract （167）      PDF（pc） （5365KB）（91）       Knowledge map    Save This study focuses on the value differences and evolutionary patterns of data assets across distinct application scenarios, proposing a practical framework for scenario-based dynamic pricing. A system dynamics model is developed based on a dual-scenario classification of “self-use” and “transaction-oriented” data applications, structured around development, application, and risk dimensions. Simulation functions are embedded to capture coupling effects and time delays. Using Vensim , multi-scenario simulations and sensitivity analyses are conducted to identify key drivers and feedback loops in the value evolution process. The simulations show that development contributes foundationally to asset growth, application value demonstrates strong amplification effects, and risk control improves system stability. Flow efficiency, value derivation, and data integrity are identified as the most elastic variables influencing value outcomes. The proposed model offers a structured and adaptive approach to valuing data assets under diverse scenarios. Related Articles | Metrics | Comments（0）

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Constructing High-Quality Enterprise Data Sets: Connotations, Characteristics, Logical Framework and Future Prospects Zhang Borui　Chen Tao　Hu Jie　Xia Yikun Journal of Information Resources Management    2026, 16 (2): 55-68.   DOI: 10.13365/j.jirm.2026.02.055 Abstract （165）      PDF（pc） （1720KB）（101）       Knowledge map    Save Analyzing the connotation, characteristics and logical framework of enterprise high-quality dataset construction is essential to unlocking data value and enabling firms' digital-intelligent transformation. This paper conceptualises enterprise high-quality datasets, identifies six core attributes, establishes a three-dimensional framework (value, scenario, technology) based on socio-technical systems theory to provide theoretical support for the construction of high-quality corporate datasets under the "AI Plus" background; subsequently, from the analytical perspectives of the data value chain and data life cycle theories, it constructs a five-dimensional model encompassing scenario demand perception, data resource weaving, knowledge resource extraction, data-knowledge integration and refinement, and a digital-intelligence service ecosystem; finally, in response to the practical dilemmas currently faced by enterprises, the study proposes targeted optimization strategies, suggesting that future efforts should focus synergistically on data scenario shaping, data aggregation and governance, data labeling optimization, and data security assurance to overcome the bottlenecks of high-quality dataset construction, enhance the value-enabling efficiency of data elements, and provide robust support for the digital-intelligence transformation and high-quality development of enterprises. Related Articles | Metrics | Comments（0）

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Research on Industrial Technology Foresight Based on Patent Value Evaluation and Topic Mining from the Perspective of Fast and Slow Patent Comparisons: Taking Smart Grid as an Example Zhu Mengping　Shi Guoliang　Gao Yimin Journal of Information Resources Management    2026, 16 (1): 101-115.   DOI: 10.13365/j.jirm.2026.01.101 Abstract （161）      PDF（pc） （4370KB）（96）       Knowledge map    Save Firstly, this study constructs a three-dimensional technology foresight framework integrating "technological value, structural relatedness, and examination timeliness" by developing a multi-dimensional patent value evaluation system, employing the entropy-weighted TOPSIS method and multiple linear regression models to derive comprehensive patent value scores and influencing factors. Second, patent datasets are categorized based on examination speed, and BERTopic model are applied to identify the technological themes, enabling a comparative analysis of their thematic distributions. Finally, a three-dimensional technology foresight mapping framework is developed, incorporating theme value, theme relevance, and average examination duration to delineate the technological prospects embedded in fast and slow patents. Taking the smart grid sector as an example, the findings reveal that within the dimension space defined by high thematic relevance and high thematic value, slow patents demonstrate stronger technological prospect potential compared to fast patents. Based on these insights, strategic recommendations are proposed to assist senior R&D managers in effectively advancing innovation. Related Articles | Metrics | Comments（0）

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Review Research on Measuring Scientific Contributions of Academic Papers: Identification, Classification, and Intensity Wei Huanan　Ding Jielan1　Liu Xiwen Journal of Information Resources Management    2025, 15 (6): 157-171.   DOI: 10.13365/j.jirm.2025.06.157 Abstract （160）      PDF（pc） （1632KB）（328）       Knowledge map    Save This study systematically reviews research related to the scientific contributions of academic papers in the field of scientometrics, summarizes research methods and the latest progress, and aims to provide reference and guidance for subsequent research. By reviewing relevant domestic and international literature, this study first defines and analyzes the core concepts and connotations of the scientific contributions of academic papers. Subsequently, from a methodological perspective, it provides a detailed description and summary of the identification, classification, and intensity measurement of scientific contributions of academic papers. This study shows that the measurement of scientific contributions of academic papers is an emerging research field that is rapidly developing and requires further development and improvement. In the future, research can deepen the theoretical study of the mechanisms underlying the scientific contributions of papers and refine the measurement methods for fine-grained scientific contributions, such as theoretical contributions, methodological contributions and data contributions, and advance the application research of large language models in measuring the scientific contributions of papers. Related Articles | Metrics | Comments（0）

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Journal of Information Resources Management    2025, 15 (6): 4-4.   Abstract （154）      PDF（pc） （294KB）（208）       Knowledge map    Save Related Articles | Metrics | Comments（0）

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Research on the Mechanism and Effect of Data Elements Empowering Urban Community Resilience from a Full Life Cycle Perspective Chen Yingxin　Ge Ran　Ping Shanshan　Ding ding Journal of Information Resources Management    2026, 16 (1): 63-77.   DOI: 10.13365/j.jirm.2026.01.063 Abstract （148）      PDF（pc） （5186KB）（145）       Knowledge map    Save In the context of the development of new quality productive forces, accelerating the digital transformation of communities to enhance community resilience has become the core orientation and fundamental demand of grassroots community governance. Based on the perspective of the whole life cycle of data, the hybrid method of Structural Equation Model (SEM) and BP Neural Network was used to empirically analyze the resilience changes of different types of communities under the influence of data elements, and reveal the mechanism and effect of data-empowered community resilience. The research results are as follows: data production, data integration and data application have different impacts on the physical, social, institutional and cultural resilience of communities, respectively. The community refined consultation governance model promotes data integration and sharing, breaks down departmental barriers and the phenomenon of "digital suspension", and improves the physical and social resilience of communities. Under the basic national conditions of China's population aging, the phenomenon of "silver digital divide" reduces the overall data utilization level of communities, resulting in a decrease in the institution resilience of communities. The dual blessing of community refined consultation governance and the development of community digital technology has made up for the shortcomings of the aging community population, and the overall level of community resilience has been improved. Related Articles | Metrics | Comments（0）

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Measurement of Data Element Circulation Level in China: Theoretical Logic, Regional Differences and Dynamic Evolution Zhang Yanhong　Zhou Lexin Journal of Information Resources Management    2026, 16 (2): 40-54.   DOI: 10.13365/j.jirm.2026.02.040 Abstract （140）      PDF（pc） （5070KB）（81）       Knowledge map    Save Data circulation is the key to promote the transformation of data into production factors, and its circulation level is the core index to measure the marketization allocation efficiency of data elements. Based on resource endowment theory and technology potential energy theory, this paper constructs an index system for evaluating the circulation level of data elements, measures the circulation level of data elements in various regions of China from 2019 to 2024, and deeply analyzes the regional differences, structural causes and spatio-temporal dynamic evolution characteristics by using Dagum Gini coefficient, variance decomposition method, kernel density estimation and Moran index. The conclusions indicate that: The circulation level of data elements in China generally presents a right-leaning distribution pattern, with obvious regional gradient characteristics of "eastern leading, central accelerating catch-up, western and northeast lagging"; the overall difference of data elements circulation level in each region is convergent, among which regional difference is the main source of overall difference, and the eastern region contributes the most to regional difference; there is obvious spatial positive aggregation characteristics in the circulation level of data elements in China, and the "low-low" aggregation characteristics in western regions are dominant. The conclusion reveals the regional differences and evolution characteristics of data elements circulation level in China, which has important value for realizing the deep circulation interaction of data elements and the integration of national data market. Related Articles | Metrics | Comments（0）

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From Interaction to Collaboration: The Evolution of Human-AI Relations in the Era of Digital Intelligence: A Book Review of Human-AI Interaction and Collaboration Xia Lixin Journal of Information Resources Management    2026, 16 (2): 166-168.   DOI: 10.13365/j.jirm.2026.02.166 Abstract （139）      PDF（pc） （517KB）（83）       Knowledge map    Save How are interaction patterns between users and artificial intelligence systems evolving in the era of digital intelligence? Through the construction of theoretical frameworks, the description of application scenarios, and the identification of potential risks, Human-AI Interaction and Collaboration provides a comprehensive analysis of the underlying logic of human-AI interaction and collaboration. The book posits "human-centered" as the paramount principle for AI systems and offers a detailed examination of how factors such as user perception, system design, and human-AI relations influence the efficacy and outcomes of collaborative synergy. While proposing mitigation strategies for potential risks, this work showcases the vast prospects of human-AI collaboration across multidisciplinary domains, including healthcare, scientific research, and finance. This work provides a theoretical foundation for future research in this field and serves as an ethical compass for AI system designers. Related Articles | Metrics | Comments（0）

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Identification of Citation Intent in Academic Literature Based on Prompt Learning with Multi-Task Learning Zhang Xiaojuan　Guo Jiarun　Wang Jiahui　Liu Yijun Journal of Information Resources Management    2026, 16 (2): 125-139.   DOI: 10.13365/j.jirm.2026.02.125 Abstract （135）      PDF（pc） （5907KB）（75）       Knowledge map    Save The automatic identification of citation intentions in academic literature contributes to a deeper understanding of academic paper content and facilitates the development of a more equitable framework for scientific research evaluation. To improve the accuracy and generalizability of citation intention recognition in low-resource scenarios, this study proposes a novel framework that integrates prompt learning and multi-task learning, First, the main task of automatic citation intent recognition is jointly trained with two auxiliary tasks: citation value identification and citation section identification. All three tasks are unified within a P-tuning-based prompt learning framework. In this setup, discrete prompt tokens are first transformed into continuous vectors by a multi-layer perceptron. These vectors are then combined with the input text to form the input sequence, effectively converting the classification task into a cloze-style prediction. Furthermore, the label word set is expanded, and a weighted averaging method is applied to select the label with the highest score as the final prediction. Finally, a soft parameter sharing mechanism is employed to achieve collaborative optimization across the three tasks for citation intent recognition. Experimental results on two publicly available datasets, ACL-ARC and SciCite, demonstrate that the proposed model significantly outperforms baseline and other prompt learning models under varying sample sizes. Additionally, the two auxiliary tasks effectively improve the accuracy and generalizability of citation intent recognition. Related Articles | Metrics | Comments（0）