# Predicting Research that will be Cited in Policy Documents

## TL;DR

This paper develops models to predict if research will be cited in policy documents using online attention metrics, highlighting the societal impact of research outputs.

## Contribution

It introduces classification models that predict policy citations based on online attention, with a comparative evaluation of different classifiers.

## Key findings

- Random Forest and Naive Bayes classifiers perform best
- Models achieve high accuracy, precision, and recall
- Online attention is a strong predictor of policy citations

## Abstract

Scientific publications and other genres of research output are increasingly being cited in policy documents. Citations in documents of this nature could be considered a critical indicator of the significance and societal impact of the research output. In this study, we built classification models that predict whether a particular research work is likely to be cited in a public policy document based on the attention it received online, primarily on social media platforms. We evaluated the classifiers based on their accuracy, precision, and recall values. We found that Random Forest and Multinomial Naive Bayes classifiers performed better overall.