[Purpose/significance] With the vigorous development of the open science movement, uncovering its enhancement effect on the international academic discourse power of Chinese papers and conducting a multi-perspective evaluation of such discourse power holds practical significance for facilitating the efficient dissemination of Chinese papers within the open science system and enhancing their international academic discourse power. [Method/process]This paper employs Chinese management papers as the sample. It constructs an evaluation index system that encompasses four key dimensions: academic originality, academic influence, academic communication power, and academic leadership power, by integrating scientometric indicators, altmetrics, and full-text bibliometric indicators. Simultaneously, the study utilizes propensity score matching to assess the enhancement effect of the open science environment on the international academic discourse power of Chinese papers. This study adopts the propensity score matching method to evaluate the promotion effect of the open science environment on the international academic discourse power of Chinese papers. It further applies the CRITIC-BP and CRITIC-TOPSIS methods to assess such discourse power from single-dimensional and comprehensive perspectives under the open science environment, and finally conducts intelligent verification of the evaluation results via CNN. [Result/conclusion] The open science environment exerts distinct promotive effects on four key dimensions of Chinese papers. Specifically, the enhancing impacts on academic recognition level, international dissemination effectiveness, and disciplinary leadership are 11.4, 2.19, and 0.1, respectively. Significant correlations are identified both among the various dimensions of the international academic discourse power of Chinese papers and between each dimension and the comprehensive evaluation result. International academic discourse power in Q1 journal papers tends to be highly concentrated. For Chinese scholars' participation in international collaborative Q1 journal papers, supportive involvement is the main form, and their leading role remains relatively limited.

[Purpose/significance] The customized generation and targeted dissemination of disinformation driven by large language models have intensified the persistence and diversity of individual cognitive biases in fact-checking processes. To address this issue, this article aims to examine the debiasing effects of LLMs in dynamic human-LLMs interactions on multiple types of cognitive biases, as well as their behavioral implications. [Method/process] A sequential mixed-methods design was adopted. Firstly, grounded theory was applied to interaction data between fact-checkers and LLMs to identify common types of cognitive biases and construct experimental variables. Subsequently, based on pretest-posttest experimental data, nonparametric statistical tests were conducted to evaluate the effects of LLMs-driven dynamic debiasing interactions on different types of cognitive biases and disinformation engagement behaviors.[Result/conclusion] Cognitive biases in fact-checking can be categorized into three core dimensions: cognitive processing deficits, selective exposure, and defensive beliefs, which exhibit dynamic and interwoven characteristics.LLMs, through personalized and evidence-based interactive communication, can effectively identify and mitigate various cognitive biases in fact-checking. Moreover, such interactions significantly reduce individuals′ willingness to "like" disinformation, while showing limited impact on promoting high-effort corrective behaviors such as commenting.

[Purpose/significance] To explore how algorithmic folk theories evolve from dispersed individual perceptions to collective consensus through multi-agent interactions, thereby providing insights into the formation process and evolutionary mechanisms of algorithmic folk theories. [Method/process] From the perspective of knowledge diffusion, this study employs an Agent-Based Modeling (ABM) simulation approach, conceptualizing information users as heterogeneous cognitive agents. By incorporating metrics such as Shannon entropy, structural entropy, and LMC complexity, it investigates the evolutionary process of algorithmic folk theories as well as the diffusion and distribution patterns of algorithmic knowledge. [Result/conclusion] The evolution of algorithmic folk theories undergoes four stages:emergence, development, stabilization, and decline. The diffusion network evolves from fragmentation to integration,while knowledge distribution exhibits three patterns: consensus, equilibrium, and polarization. Individual cognitive ability and foundational knowledge level are identified as key factors influencing the evolution of folk theories, and excessive disparities may lead to knowledge gaps and cognitive polarization.

[Purpose/significance] The increase in cognitive load during generative information search may lead to user frustration and system-switching behaviour. This paper aims to investigate the patterns and causes of changes in users′ cognitive load at different stages of generative information search. [Method/process] Cognitive load data was collected using experimental methods whilst users were performing search tasks. Cognitive load was measured using subjective, task performance and physiological measurement methods, and a one-way analysis of variance combined with Welch′s test was employed to determine whether there were significant differences in cognitive load across different stages of the search process. Subsequently, subjective descriptions from users were gathered through interviews following the experiment, and thematic analysis was used to identify the causes of cognitive load during the search process.[Result/conclusion] Users′ cognitive load is heaviest during the post-focus stage, followed by the pre-focus stage,with the lightest load occurring during the focus stage. Cognitive load primarily stems from four sources: the user themselves, the task, the generative AI system, and the interaction between the user and the system.

[Purpose/significance] The trend of age-related diseases manifesting at younger ages is becoming increasingly prominent. To address the issue of health information anxiety among young groups resulting from this trend, it is essential to investigate its underlying mechanisms. [Method/process] Drawing on the health belief model and the MOA (Motivation-Opportunity-Ability) theory, this study proposed 12 research hypotheses and constructed a model of the formation mechanism of health information anxiety among young groups. It then distributed a questionnaire survey,which yielded 401 valid responses. Finally, the study utilized AMOS software for data analysis and model testing. [Result/conclusion] Perceived susceptibility has a significant positive effect on health information anxiety among young groups, while self-efficacy exhibits a significant negative effect. In contrast, perceived severity and perceived barriers are not directly associated with health information anxiety. Perceived susceptibility fully mediates the relationship between health status and health information anxiety. Self- efficacy fully mediates the two parallel pathways linking health information quality and health information alienation to health information anxiety. Additionally, self-efficacy partially mediates the relationship between health information literacy and health information anxiety.

[Purpose/significance] Under the background of the nationwide healthy digital intelligence construction and the healthy China construction during the "Tenth Five Year Plan" period, it is equally important to ensure the twoway smooth access and exit of the elderly to promote healthy aging. This paper aims to identify and relieve the elderly people′s active digital disconnection barriers, and ensure their right to independently regulate their digital life. [Method/process] First, 124 elderly people over 60 years old with disconnection practice/willingness were interviewed, and the types of barriers were mined from 117 interview data by topic analysis method; another 20 elderly people were recruited for 30 days of disconnection practice. The snowNLP emotion analysis model was used to quantitatively analyze 69 self-report data obtained by mobile experience sampling method, and identify behavior characteristics. [Result/conclusion] There were cognitive, behavioral and environmental barriers in the process of active digital disconnection in the elderly people; the behavior shows three characteristics: individual debugging differences, active coping role and internal and external double bondage. Cognitive empowerment, behavioral support and environmental tolerance are three relief strategies to enhance endogenous motivation, strengthen coping effectiveness and break the double dilemma.

[Purpose/significance] The sustainable development of knowledge payment platforms relies heavily on the active participation of both knowledge producers and knowledge consumers. Accurately identifying these two types of key users is crucial for optimizing platform operations and fostering a healthy content ecosystem. [Method/process]Based on the context of knowledge payment platforms, this study proposes a key user identification framework that integrates multi-dimensional features. The framework characterizes user behavior from three dimensions: activity, professionalism, and commercial value. It incorporates BERT-based sentiment analysis to quantify the emotional tendencies in user interactions. Furthermore, a sentiment-weighted LeaderRank algorithm is employed to calculate sentiment scores, and the entropy weight method is used to determine indicator weights. Finally, a comprehensive key user identification system is constructed by integrating these multi-dimensional features, generating a final ranking of user criticality. [Result/conclusion] Empirical research on the Ximalaya FM platform demonstrates that the proposed method effectively identifies top knowledge producers with high commercial value, while also capturing vertical domain experts and sentiment-driven long-tail users. The identification results are highly consistent with the platform′s ecological structure. Key knowledge consumers can be classified into three categories: core-value type, complementary-advantage type, and long-tail potential type, reflecting differentiated behavioral patterns under multi-dimensional features.This provides an empirical basis and methodological support for platforms to implement precise hierarchical user management.

[Purpose/significance] By systematically identifying key factors influencing algorithm adoption, this study constructs an integrated theoretical model to deeply reveal its underlying behavioral generation mechanism, addressing the current fragmented landscape of algorithm adoption research and the lack of a systematic theoretical framework.[Method/process] A research approach combining systematic literature review and procedural grounded theory was employed. Following PRISMA guidelines, a systematic search and screening were conducted on literature from two core databases, Web of Science and CNKI, ultimately including 77 articles. Through procedural analysis involving open coding, axial coding, and selective coding, the content of these articles was summarized, refined, and integrated to identify core influencing factors and build the theoretical model. [Result/conclusion] The study identified 17 key factors influencing algorithm adoption and developed an integrated influencing factors model comprising four dimensions: external environment, algorithmic technology, subject, and task fit. This model reveals that algorithm adoption is a dynamically driven, multi-dimensional process: the external environment provides institutional constraints and social support; algorithmic technology attributes determine its intrinsic efficacy and trustworthiness; the subject constitutes the internal driving system for adoption behavior; and task fit acts as a critical nexus connecting all dimensions and determining the ultimate realization of adoption behavior.

[Purpose/significance] Systematically identifying the hierarchical structure and evolutionary patterns of users′ role cognition of Generative Artificial Intelligence (GenAI) in academic topic selection not only enriches the theoretical framework of human-AI collaboration but also offers implications for layered interaction design of GenAI and for cultivating students′ academic innovation capabilities in the AI for science era. [Method/process] Based on the Computers Are Social Actors theory and role theory framework, this study employed a longitudinal interpretative phenomenological analysis to track 13 university students′ interactions with GenAI and their cognitive experiences during academic topic selection over three months through 36 in-depth interviews. [Result/conclusion] This study, drawing on Bloom′s taxonomy, developed a multi-level 3M (Minion, Mate, and Mentor) role spectrum comprising four hierarchical layers of role type, role dimension, specific role, and role manifestation. Users′ role perceptions of GenAI exhibit complex diversity; GenAI′s role positioning undergoes dynamic evolution as research progresses, and users′ role perceptions of GenAI present three dynamic evolutionary patterns: elevation, expansion, and adaptation.

[Purpose/significance] Exploring the formation mechanism of cognitive illusions induced by AI algorithms in the "time-sink" phenomenon and clarifying its impact on intelligence perception and public opinion communication is of great significance for the governance of the information ecosystem in the digital era. [Method/process] A multi-dimensional analytical framework is constructed based on multi-source heterogeneous data. Quantitative indicators such as expansion coefficient and attention drift rate are proposed. Comprehensive modeling and case analysis are adopted to conduct empirical research, and a closed-loop intervention mechanism for intelligence perception is established.[Result/conclusion] Based on the analysis results, strategies for blocking cognitive illusions are proposed from three dimensions: algorithm optimization, user guidance and community governance, aiming to provide references for algorithm optimization, improvement of users' media literacy and public opinion risk governance.

[Purpose/significance] This study aims to identify the influencing factors of the information disclosure quality in natural disaster emergency from the perspective of civil servants, clarify the relationships among these influencing factors, and thereby support the government in enhancing information disclosure quality to cope with disaster risks.[Method/process] Based on the TOE framework and related literature, a model for influencing factors of government information disclosure quality was initially constructed. Through semi-structured in-depth interviews with civil servants, the original model was revised and a new influencing mechanism model applicable to the natural disaster emergency was established. [Result/conclusion] The influencing factors of government information disclosure quality in natural disaster emergency include six technological factors, ten organizational factors, and six environmental factors.The interaction pathways among different categories of factors can be summarized into "technology→organization", "organization→technology", "organization→organization", "environment→technology", "environment→organization",and"environment→environment".