Data and information management最新文献

The emphasis on social phenomena that defines the Everyday Information Practice (EIP) domain sets it apart from information behavior fields. This study highlights the importance of researching everyday information practices in contemporary social-cultural contexts by using Savolainen's EIP-related models as examples. A synopsis of the characteristics of earlier studies in terms of research contexts, participants, research questions, and research methods was created by evaluating the pertinent studies using EIP-related models. A trend of social responsibility-focused EIP research was presented, along with recommendations for future research in the field of EIP from the perspectives of participants and research methods.

Internet use has resulted in the flow and interweaving of risks and increased the difficulty of risk governance. Strengthening public risk perception research can not only make up for the shortcomings of traditional government-centered risk governance research but also improve the ability of risk governance. By employing data from Chinese Social Survey (CSS) and the mediating test with the process plug-in in SPSS, this paper tries to explore the influence mechanism of Internet use on public risk perception, as well as the mediating effect of different types of political participation. The results show that Internet use has a significantly positive impact on comprehensive public risk perception. Network political participation has significantly enhanced the public risk perception, while traditional political participation has significantly reduced the public risk perception. Besides, network political participation plays a mediating role in the relationship between Internet use and public risk perception.

In the field of machine learning, the issue of class imbalance is a common problem. It refers to an imbalance in the quantity of data collected, where one class has a significantly larger number of data compared to another class, which can negatively affect the classification efficiency of algorithms. Under-sampling methods address class imbalance by reducing the quantity of data in the majority class, thereby achieving a balanced dataset and mitigating the class imbalance problem. Traditional under-sampling methods based on k-means clustering either set the unified value of k (number of clusters) or determine it directly based on the quantity of data in the minority or majority class. This paper proposes an adaptive k-means clustering under-sampling algorithm that calculates an appropriate k for each dataset. After clustering the majority class dataset into k clusters, our algorithm calculates the distances between the data within each cluster and the cluster centroids from two perspectives and selects data based on these distances. Subsequently, the subset of the majority class dataset are combined with the minority class dataset to generate a new balanced dataset, which is then used for classification algorithms. The performance of our algorithm is evaluated on 45 datasets. Experimental results demonstrate that our algorithm can dynamically determine appropriate k for different datasets and output a balanced dataset, thus enhancing the classification efficiency of machine learning algorithms. This work can provide new algorithmic ensemble strategies for addressing class imbalance problem.

The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.

There are productivity differences among academic fields. Researchers who work in academic fields that have low productivity are pressured to publish more, and this policy may cause researchers to publish more in journals that have lenient standards and publish articles that are not necessarily valuable for their academic field. The problem is not solved by normalizing journals’ impact factors by the subjects because the normalized impact factors do not reflect the difficulty of publication in that subject. In this paper, we propose an evaluation method –Reference Group Similarity Index-that addresses the productivity differences issue. The method uses the publications of a reference group of departments that are believed to have the right publication incentives. Then, other departments are evaluated to the degree that their publications are similar to that of the reference group. We apply the method to the top 50 economics departments according to USNews rankings and show that the department rankings that we get from the Reference Group Similarity Index are largely consistent with the USNews Rankings.

This paper investigates applying AI models and topic modelling techniques to enhance computational literature reviews in business, management, and information systems. The study highlights the significance of impactful journals and emphasises the need for interdisciplinary and transdisciplinary research, especially in addressing AI's ethical and regulatory challenges. We demonstrate the effectiveness of combining machine learning and ChatGPT in the literature review process. Machine learning is used to identify research topics, and ChatGPT assists researchers in labelling the topics, generating content, and improving the efficiency of academic writing. By leveraging topic modelling techniques and ChatGPT, we uncover and label topics within the literature, shedding light on the thematic structure and content of the research field, allowing researchers to uncover meaningful insights, identify research gaps, and highlight rapidly expanding research areas. Additionally, we contribute to the literature review process by introducing a methodology that identifies impactful papers, helping to bridge the gap between computational literature reviews and traditional literature reviews.