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The purpose of this study was to investigate the participants' attitudes toward the ethical issues caused by collecting social media data (SMD) for research, as well as the effects of familiarity, trust and altruism on the participants' attitudes toward the ethics of SMD research. It is hoped that through this study, scholars will be reminded to respect participants and engage in ethical reflection when using SMD in research.

This study adopted social media users as its research subjects and used Sina Microblog, the world's largest Chinese social media platform, as the example. Based on the 320 valid responses collected from a survey, structural equation modeling was employed to examine the research model.

The results indicated that altruism, familiarity and trust have significant influences on participants' attitudes toward the ethics of SMD research, and familiarity also influences attitudes through the mediating role of trust and altruism.

This study explored the mechanism underlying the relationship between the determining factors and participants' attitudes toward the ethics of SMD research, and the results demonstrated that the informed consent mechanism is an effective way to communicate with participants and that the guiding responsibility of the platform should be improved to standardize SMD research.

The rising volume of open government data (OGD) contrasts with the limited acceptance and utilization of OGD among citizens. This study investigates the reasons for citizens’ not using available OGD by comparing citizens’ attitudes towards OGD with the development of OGD portals. The comparison includes four OGD utilization processes derived from the literature, namely OGD awareness, needs, access and consumption.

A case study in China has been carried out. A sociological questionnaire was designed to collect data from Chinese citizens (demand), and personal visits were carried out to collect data from OGD portals (supply).

Results show that Chinese citizens have low awareness of OGD and OGD portals. Significant differences were recognized between citizens’ expectations and OGD portals development in OGD categories and features, data access services and support functions. Correlations were found between citizens’ OGD awareness, needs, access and consumption.

By linking the supply of OGD from the governments with each process of citizens’ OGD utilization, this paper proposes a framework for citizens’ OGD utilization lifecycle and provides a new tool to investigate reasons for citizens’ not making use of OGD.

To address the key problem of lack of use in the advancement of open government data (OGD) portals from the aspect of good usability, which is an essential prerequisite to the acceptance and usage of a portal, this paper aims to develop a usability framework including design principles and criteria for OGD portals and to discover problems in the present usability design.

This study builds the usability framework by extending usability principles for general websites to address the specific needs of OGD portals. Criteria for each principle are developed accordingly based on the literature. A comparative heuristic evaluation involving five expert evaluators and 13 Chinese province-level OGD portals has been carried out to test the capability of the usability framework.

A usability framework with 24 principles and 63 criteria has been built. The heuristic evaluation shows OGD portals performed better in meeting general principles than the OGD portals specific ones. Insufficient help functions weakened OGD portals' usability. Similarities and differences were found of Chinese OGD portals compared with similar studies in the United States.

This paper proposed a usability framework for OGD portals and proved its capability in recognizing usability problems and its causes by carrying out a comparative heuristic evaluation in China. By comparing the evaluation results with other studies in the United States, the findings and lessons learnt in this study can thus be shared across international borders.

The purpose of this paper is to understand how Chinese library and information science (LIS) journal articles cite works from outside the discipline (WOD) to identify the impact of knowledge import from outside the discipline on LIS development.

This paper explores the Chinese LIS’ preferences in citing WOD by employing bibliometrics and machine learning techniques.

Chinese LIS citations to WOD account for 29.69 percent of all citations, and they rise over time. Computer science, education and communication are the most frequently cited disciplines. Under the categorization of Biglan model, Chinese LIS prefers to cite WOD from soft science, applied science or nonlife science. In terms of community affiliation, the cited authors are mostly from the academic community, but rarely from the practice community. Mass media has always been a citation source that is hard to ignore. There is a strong interest of Chinese LIS in citing emerging topics.

This paper can be implemented in the reformulation of Chinese LIS knowledge system, the promotion of interdisciplinary collaboration, the development of LIS library collection and faculty advancement. It may also be used as a reference to develop strategies for the global LIS.

This paper fills the research gap in analyzing citations to WOD from Chinese LIS articles and their impacts on LIS, and recommends that Chinese LIS should emphasize on knowledge both on technology and people as well as knowledge from the practice community, cooperate with partners from other fields, thus to produce knowledge meeting the demands from library and information practice as well as users.

With the advent of the intelligent environment, as novice researchers, graduate students face digital challenges in their research topic selection (RTS). The purpose of this paper is to explore their cognitive processes during data-driven decision making (DDDM) in RTS, thus developing technical and instructional strategies to facilitate their research tasks.

This study developes a theoretical model that considers data-driven RTS as a second-order factor comprising both rational and experiential modes. Additionally, data literacy and visual data presentation were proposed as an antecedent and a consequence of data-driven RTS, respectively. The proposed model was examined by employing structural equation modeling based on a sample of 931 graduate students.

The results indicate that data-driven RTS is a second-order factor that positively affects the level of support of visual data presentation and that data literacy has a positive impact on DDDM in RTS. Furthermore, data literacy indirectly affects the level of support of visual data presentation.

These findings provide support for developers of knowledge discovery systems, data scientists, universities and libraries on the optimization of data visualization and data literacy instruction that conform to students’ cognitive styles to inform RTS.

This paper reveals the cognitive mechanisms underlying the effects of data literacy and data-driven RTS under rational and experiential modes on the level of support of the tabular or graphical presentations. It provides insights into the match between the visualization formats and cognitive modes.