Exploring Features for Predicting Policy Citations

TL;DR

This paper investigates how altmetrics, especially tweet counts, can predict whether research papers are cited in public policy, achieving high accuracy with an ROC AUC of 0.91.

Contribution

It evaluates the predictive power of altmetrics for policy citations and identifies tweet count as the most effective feature.

Findings

Tweet count yields the highest prediction accuracy (AUC=0.91).

Altmetrics can be useful indicators for policy citation prediction.

Receiver operating characteristic analysis validates the effectiveness of features.

Abstract

In this study we performed an initial investigation and evaluation of altmetrics and their relationship with public policy citation of research papers. We examined methods for using altmetrics and other data to predict whether a research paper is cited in public policy and applied receiver operating characteristic curve on various feature groups in order to evaluate their potential usefulness. From the methods we tested, classifying based on tweet count provided the best results, achieving an area under the ROC curve of 0.91.