Quantifying the Topic Disparity of Scientific Articles

被引:0
|
作者
Kim, Munjung [1 ]
Yoon, Jisung [2 ]
Jung, Woo-Sung [3 ]
Kim, Hyunuk [4 ]
机构
[1] Pohang Univ Sci & Technol, Dept Phys, Pohang, South Korea
[2] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Pohang, South Korea
[3] Pohang Univ Sci & Technol, Dept Ind & Management Engn, Dept Phys, Grad Sch Artificial Intelligence, Pohang, South Korea
[4] Boston Univ, Metropolitan Coll, Dept Adm Sci, Boston, MA 02215 USA
关键词
Neural embedding techniques; BERT; Microsoft Academic Graph; CITATION; IMPACT;
D O I
10.1145/3487553.3524655
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Citation count is a popular index for assessing scientific papers. However, it depends on not only the quality of a paper but also various factors, such as conventionality, journal, team size, career age, and gender. Here, we examine the extent to which the conventionality of a paper is related to its citation count by using our measure, topic disparity. The topic disparity is the cosine distance between a paper and its discipline on a neural embedding space. Using this measure, we show that the topic disparity is negatively associated with citation count, even after controlling journal impact, team size, and the career age and gender of the first and last authors. This result indicates that less conventional research tends to receive fewer citations than conventional research. The topic disparity can be used to complement citation count and to recommend papers at the periphery of a discipline because of their less conventional topics.
引用
收藏
页码:769 / 773
页数:5
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