《通用计算机科学杂志》网络特性、可读性特性与传统文献计量学特性的比较

Diego Jacobs, A. Bobic, C. Gütl
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引用次数: 0

摘要

为了更好地理解单一期刊背景下的出版数据,并有可能提供科学作者的表现和预计论文质量的替代测量作为第一步,本工作通过社交媒体分析和自然语言处理技术分析期刊数据。本文描述了通过创建共同作者网络和计算多节点属性来丰富和分析文献计量数据的过程,并与传统的文献计量测量方法进行了比较。此外,我们还提取了群落,并将这些群落中作者的平均文献计量属性与各种群落属性进行了比较。最后,计算摘要和标题的长度和可读性,并与各自论文的被引次数进行比较。上述数值的比较并没有显示任何数值之间有很强的相关性。然而,其中一些属性有轻微的相关性。分析表明,单一期刊合著网络不足以提取作者或论文学术表现的有意义的替代测量。然而,它也表明网络属性和可读性度量可以潜在地成功地利用更大的数据集提取替代性能指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Network and Readability Properties With Traditional Bibliometric Properties in the Journal of Universal Computer Science
To better understand publication data in the context of a single journal and potentially provide alternative measurements of scientific authors' performance and projected paper quality as a first step, this work analyzes journal data through social media analysis and natural language processing techniques. This paper describes the process of enriching and analyzing bibliometric data by creating a co-author network and calculating multiple node properties, which are compared to traditional bibliometric measurements. Furthermore, communities are extracted, and the averaged bibliometric properties of authors in those communities are compared to various community properties. Finally, the abstract and title length and readability were calculated and compared to the citation counts of respective papers. The comparison of the aforementioned values did not indicate a strong correlation among any of the values. However, some of the properties were slightly correlated. The analysis reveals that a single journal co-authorship network is not enough to extract meaningful alternative measurements for academic performance of authors or papers. However, it also indicates that network properties and readability measures could be potentially successfully leveraged to extract alternative performance indicators with a larger dataset.
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