Showing items 1 - 3 of 3

Data-driven computational social science : A survey

- Zhang, Jun, Wang, Wei, Xia, Feng, Lin, Yu-Ru, Tong, Hanghang

  • Authors: Zhang, Jun , Wang, Wei , Xia, Feng , Lin, Yu-Ru , Tong, Hanghang
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Big Data Research Vol. 21, no. (2020), p. 1-22
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  • Description: Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on datadriven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.

Raising awareness of fall risk among Chinese older adults : Use of the home fall hazards assessment tool

- You, Liming, Deans, Cecil, Liu, Ke, Zhang, Meifen, Zhang, Jun

  • Authors: You, Liming , Deans, Cecil , Liu, Ke , Zhang, Meifen , Zhang, Jun
  • Date: 2004
  • Type: Text , Journal article
  • Relation: Journal of Gerontological Nursing Vol. 30, no. 6 (2004), p. 35-42
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  • Description: The purpose of this study was to evaluate the effectiveness of the Home Fall Hazards Assessment (HFHA) tool adjusted for Chinese homes to raise awareness of the dangers of falling among Chinese older adults. This descriptive study of 415 Chinese adults age 60 and older was completed in the older adults’ homes in the community. The study was an assessment by nurses of hazards in the home setting using the HFHA. The hazards identified by nurses were compared with those reported by the elderly individuals. The study identified the most frequently found hazards in the homes and showed that a large discrepancy existed between the actual hazards assessed by trained nurses using the HFHA and the perceived hazards reported by the participants (p < .001). The study suggests that the HFHA can be an effective health education method and that the involvement of older adults in the assessment process will effectively raise their awareness of existing risk factors for falls in their everyday life.
  • Description: C1
  • Description: 2003000856

The gene of scientific success

- Kong, Xiangjie, Zhang, Jun, Zhang, Da, Bu, Yi, Ding, Ying, Xia, Feng

  • Authors: Kong, Xiangjie , Zhang, Jun , Zhang, Da , Bu, Yi , Ding, Ying , Xia, Feng
  • Date: 2020
  • Type: Text , Journal article
  • Relation: ACM Transactions on Knowledge Discovery from Data Vol. 14, no. 4 (2020), p.
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  • Description: This article elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, discovering potential cooperators, and the like. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard work. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our article presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other. © 2020 ACM.
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