The Citation Behavior of Statisticians

• Activity Number: 11 - Modern Machine Learning Tools for Social Science
• Type: Invited
• Date/Time: Sunday, August 7, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Social Statistics Section
• Author(s): Jiashun Jin* and Tracy Ke and Pengsheng Ji and Wanshan Li
• Companies: Carnegie Mellon University and Harvard University and University of Georgia and Carnegie Mellon University
• Keywords: text learning ; topic ranking ; citation prediction ; citation pattern ; journal ranking ; networks

Abstract

We collected and cleaned a data set consisting of the bibtex and citation data of 83K papers published in 36 journals in statistics and related fields, spanning 41 years. The data set provides a great opportunity to study the citation behavior, trends, and patterns of statisticians. We are interested in (a) how to identify representative research topics in statistics, rank them, and use them to visualize the dissemination of ideas across different topics, (b) how to rank all the 36 journals, (c) how to identify the friendliest journal for a given topic, and (d) how to predict future citations and identify representative citation patterns. In this talk, we propose to jointly model the bibtex and citation data by the Hofmann-Stigler model, and propose to use the TR-SCORE (among others) as a new approach to address these problems. A good understanding of these problem may help administrators in decision making, and individual authors for making plans for future research.