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.