# Predicting Patent Citations to measure Economic Impact of Scholarly Research

## TL;DR

This paper develops models to predict patent citations using social media data, aiming to assess the economic impact of scholarly research and assist researchers in identifying practical applications.

## Contribution

It introduces a novel approach of predicting patent citations from social media features, linking online activity to potential economic impact of research.

## Key findings

- Social media features can effectively predict patent citations.
- Prediction models outperform baseline methods.
- Potential to guide researchers in patent application decisions.

## Abstract

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.