谷歌浏览器插件
订阅小程序
在清言上使用

Quantifying the Topic Disparity of Scientific Articles

COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION(2022)

引用 1|浏览12
暂无评分
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.
更多
查看译文
关键词
Neural embedding techniques,BERT,Microsoft Academic Graph
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要