Mishra wins award for best student paper

Shubhanshu Mishra
Shubhanshu Mishra

Doctoral student Shubhanshu Mishra won the Best Student Paper Award at the Workshop on Informetric and Scientometric Research (SIG/MET), which was held on November 10 in conjunction with the ASIS&T 2018 Annual Meeting in Vancouver, Canada. Sponsored by Elsevier, the award recognizes achievement in student presentation in the following criteria: design of the study, originality, relevance to the workshop, and adherence to research ethics. 

Mishra presented the paper, "Expertise as an Aspect of Author Contributions," which he coauthored with Associate Professor Vetle Torvik, Associate Professor and PhD Program Director Jana Diesner, and Brent Fegley (MS/LIS '10, PhD Informatics '16). The paper is an extended abstract based on ongoing work supported by the National Institute on Aging of the NIH (Award Number P01AG039347) and the Directorate for Education and Human Resources of the NSF (Award Number 1348742). 

According to the researchers, "Authors contribute a wide variety of intellectual efforts to a research paper, ranging from initial conceptualization to final analysis and reporting, and many journals today publish the allocated responsibilities and credits with the paper. An overarching yet unreported aspect of these responsibilities is relevant expertise; that is, past experience and knowledge about the phenomenon under study and the context/techniques used to study it."

In their study of 10.2 million papers in MEDLINE published during 1980-2009, they found that the relative expertise contributions on multi-author papers varied systematically by career stage and author position. Mishra's online tool, Legolas, allows the user to see the expertise contributions for any MEDLINE article. Additionally, it provides a temporal profile of expertise contributions for any author in the Author-ity 2009 dataset.

Mishra holds an integrated MS and BS in mathematics and computing from the Indian Institute of Technology Kharagpur. He is interested in the extraction and analysis of information in social networks, such as those found in scholarly data and social media platforms. His prior projects have included systems for user sentiment profiling, active learning using human-in-the-loop design pattern, and novelty and self-citation profiling in scholarly data.

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