Predicting Patent Citations to measure Economic Impact of Scholarly Research
This work addresses the challenge of quantifying economic impact for researchers and funding agencies, though it appears incremental as it applies existing methods to new data.
The paper tackles the problem of measuring the economic impact of scholarly research by predicting patent citations using social media features and classification models, achieving predictions that can help researchers identify practical applications for their work.
A crucial goal of funding research and development has always been to advance economic development. On this basis, a consider-able body of research undertaken with the purpose of determining what exactly constitutes economic impact and how to accurately measure that impact has been published. Numerous indicators have been used to measure economic impact, although no single indicator has been widely adapted. Based on patent data collected from Altmetric we predict patent citations through various social media features using several classification models. Patents citing a research paper implies the potential it has for direct application inits field. These predictions can be utilized by researchers in deter-mining the practical applications for their work when applying for patents.