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Revealing interdisciplinary patterns is a cornerstone for the continued evolution of research, education, and societal progress, providing a scaffold upon which to build a more collaborative and integrated approach to knowledge creation. This study presents a novel approach to identifying and analyzing the critical year for interdisciplinary citations (CYIC), which was defined as the year in which qualitative change in interdisciplinary knowledge flow occurred. We conducted two experiments using a Chinese paper dataset spanning 106 disciplines from 1992 to 2022, with the first to pinpoint the occurrence of CYICs and the second to examine three patterns of interdisciplinarity following these CYICs. Our findings revealed that 85% of disciplines exhibit CYICs, often corresponding with a transition from unidirectional output to reciprocal knowledge cooperation. Furthermore, we found that datasets after CYICs are generally characterized by increased interdisciplinarity of knowledge, albeit without a corresponding rise in the interdisciplinarity of disciplines or interdisciplinary diversity. Our results suggest that policy shifts and societal needs are pivotal in driving the formation of interdisciplinary collaborations, as exemplified by the surge in mutual interdisciplinary citations in response to China's poverty alleviation efforts and western development policies. [ABSTRACT FROM AUTHOR]

How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments. [ABSTRACT FROM AUTHOR]

As a healthcare ICT4D solution, mobile health (mHealth) can potentially improve users' well‐being during pandemics, especially in developing countries with limited healthcare resources. Recent ICT4D research reveals that providing end‐users with access to ICT is insufficient for improving well‐being and, thus, understanding how mHealth empowers end‐users to enhance well‐being against stressful events is important. However, prior research has rarely discussed the issue of empowerment in the domain of mHealth or the context of major disruptive events. This paper contributes to the literature by conceptualizing the psychological empowerment of mHealth users (PEMU) and investigating its nomological network during pandemics. Drawing upon theories of psychological empowerment and event characteristics, we developed a research model and tested it through a mixed‐methods investigation, containing a quantitative study with 602 Chinese mHealth users during COVID‐19 and a follow‐up qualitative study of 326 online articles and reviews. We found that PEMU, driven by three technological characteristics (perceived response efficacy, ease of use, and mHealth quality), affects well‐being through both (a) a stress‐buffering effect, which counterbalances the detrimental, stress‐increasing effects of event criticality and disruption, and (b) a vitality‐stimulating effect, which is intensified by event criticality. These findings have important implications for ICT4D research and practice. [ABSTRACT FROM AUTHOR]

This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey—and the region of Flanders in Belgium. With the exception of Italy, top‐tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top‐tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top‐tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top‐tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric‐informed journal rankings, take into account quality dimensions that are not covered by JIFs. [ABSTRACT FROM AUTHOR]

There is increasing evidence that citations to Chinese research publications are rising sharply. A series of reasons have been highlighted in previous studies. This research explores another possibility-whether there is a 'clubbing' effect in China's surge in research citations, in which a higher rate of internal citing takes place among influential Chinese researchers. Focusing on the most highly cited research articles in nanotechnology, we find that a larger proportion of Chinese nanotechnology research citations are localized within individual, institutional, and national networks within China. Both descriptive and statistical tests suggest that highly cited Chinese papers are more likely than similar U.S. papers to receive internal and localized citations. Tentative explanations and policy implications are discussed. [ABSTRACT FROM AUTHOR]

Social influence has a great impact on human behavior, which has been widely investigated in various research fields. Even so, it has rarely been investigated in the online health community. In this paper, we focus on the multichannel access in online health communities, defining social influence as the average degree of multichannel access to a physician's colleagues. Based on the multinomial logistic regression model, we examined the direct effects of social influence and patients' rating to multichannel access. In addition, we explored the moderating effect of social influence on the relationship between patients' rating and multichannel access in online health communities. The results of the experiment and robustness testing support the propositions that social influence and patients' rating significantly and positively affect multichannel access in an online health community. The moderating effect of social influence is negative and significantly influences the accessible channels provided by the focal physician. This research contributes to the literature concerning online health communities, social influence, and multichannel access; it also has practical implications. [ABSTRACT FROM AUTHOR]

Opinion words are crucial information for sentiment analysis. In some text, however, opinion words are absent or highly ambiguous. The resulting implicit opinions are more difficult to extract and label than explicit ones. In this paper, cutting-edge machine-learning approaches - deep neural network and word-embedding - are adopted for implicit opinion mining at the snippet and clause levels. Hotel reviews written in Chinese are collected and annotated as the experimental data set. Results show the convolutional neural network models not only outperform traditional support vector machine models, but also capture hidden knowledge within the raw text. The strength of word-embedding is also analyzed. [ABSTRACT FROM AUTHOR]

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

This paper proposes a diffusion model to measure the evolution of stakeholders' disaster perceptions by integrating a disaster message model, a stakeholder model, and a stakeholder memory model, which collectively describe the process of information flow. Simulation results show that the rate of forgetting has a significantly negative effect on stakeholders' perceptions and the incremental increase in the number of affected individuals has a positive effect on the maximum level of stakeholders' perceptions, but negative effect on the duration of stakeholders' perceptions. Additionally, a delay effect, a stagnation effect, and a cumulative effect exist in the evolution of stakeholders' perceptions. There is a spike at the beginning of the profile of stakeholders' perceptions in the Damped Exponential Model. An empirical test supports the validity of this model of stakeholders' disaster perceptions. [ABSTRACT FROM AUTHOR]

Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo. [ABSTRACT FROM AUTHOR]