Bibliometrics & Altmetrics (Papers)

Session chair: Hong Huang


Awareness and Usage of Altmetrics: A User Survey
Htet Htet Aung, Mojisola Erdt, & Yin-Leng Theng

As the way of evaluating research outputs has gradually changed, and the dissemination of scholarly materials online has gotten easier, research studies investigating the awareness and usage of social media in academia have also been on the rise. The number of user surveys on the awareness and usage of altmetrics (emerging alternative metrics based on social media) has, however, remained low. This paper presents the findings of an online survey, which are the results of the first phase data collection of a user survey on the awareness and usage of altmetrics in academia. Our findings show that article views and downloads from online digital libraries or repositories are very well-known. The most used altmetrics are mentions and shares on social networks. Mentions in blog posts and topics in a forum are also popular altmetrics. Interestingly, this study reveals a tendency for non-faculty staff to be more aware of altmetrics. Furthermore, we also investigate how the usage of social media correlates with the usage of altmetrics. The findings show medium to large correlations between the usage of social media and the usage of altmetrics, which means academics who use social media often, also tend to use altmetrics often.


Exploring Prestigious Citations Sourced from Top Universities across Disciplines
Feiheng Luo, Aixin Sun, Mojisola Erdt, Aravind Sesagiri Raamkumar, & Yin-Leng Theng

There have been many studies on the factors influencing paper citation counts. A number of studies have focused on the citing papers, and corresponding methods were proposed to measure the prestige of citations based on the journal impact factors, the total citation counts and the PageRank algorithm values. However, there are drawbacks to these methods. In this paper, we proposed a novel method to identify prestigious citations from the affiliation of the citing paper. Specifically, if the authors of the citing paper are affiliated with a prestigious university, the citing paper could be counted as a prestigious citation. As a pilot, we used the top 200 universities on the QS World University Rankings 2015 to identify the prestigious universities so that the prestigious citations, named as QS citations, were identified accordingly. Experimental results validated that QS citations have more important impact on the cited papers than other citations. Papers with QS citations have better performance across most disciplines not only in total citation counts, but also in altmetrics such as the Altmetric Attension Score and Mendeley reader counts.


Yet another Method for Author Co-citation Analysis: A New Approach based on Paragraph Similarity
Tsung-Ming Hsiao & Kuang-hua Chen

Co-citation analysis has been widely adopted to represent the intellectual structure of a discipline. In general, all co-cited author pairs are regarded equal. With the development of computer technology and easy accessibility of machine readable full-text articles, new weighting schemes for measuring co-citation strength (CCS) have been proposed. However, in previous studies, only distance and sentence similarity are used to adjust CCS when applying co-citation analysis. In this study, we propose a new approach to measuring CCS based on paragraph similarity. Investigation was carried out to compare our approach and traditional author co-citation analysis (TACA), as well as other different parametric ACAs. Preliminary results show that TACA and distance-based ACA (DACA) share many commonalities. In contrast, similarity-based ACAs reveal the different structure from that of TACA and DACA. However, differences in resulting network structure were still found between paragraph-similarity-based ACA (PACA) and sentence-similarity-based ACA (SACA). Compared to SACA, PACA produces less number of factors and clusters and moderate size of clusters.

Paper Sessions
Date: October 30, 2017 Time: 1:30 pm - 3:00 pm Htet Htet Aung Feiheng Luo Tsung-Ming Hsiao