Linking Health News to Research Literature

TL;DR

This paper presents a novel method for linking health news articles to scientific research by enhancing metadata extraction and search techniques, significantly improving accuracy over existing methods.

Contribution

The paper introduces an integrated approach combining advanced named-entity recognition and a specialized elastic search engine for better linking of news and research literature.

Findings

Achieved top-1 accuracy of 0.89 on paired datasets

Demonstrated at least 0.97 top-1 accuracy on health news from EurekAlert!

Outperformed baseline methods significantly in linking accuracy

Abstract

Accurately linking news articles to scientific research works is a critical component in a number of applications, such as measuring the social impact of a research work and detecting inaccuracies or distortions in science news. Although the lack of links between news and literature has been a challenge in these applications, it is a relatively unexplored research problem. In this paper we designed and evaluated a new approach that consists of (1) augmenting latest named-entity recognition techniques to extract various metadata, and (2) designing a new elastic search engine that can facilitate the use of enriched metadata queries. To evaluate our approach, we constructed two datasets of paired news articles and research papers: one is used for training models to extract metadata, and the other for evaluation. Our experiments showed that the new approach performed significantly better than a baseline approach used by altmetric.com (0.89 vs 0.32 in terms of top-1 accuracy). To further demonstrate the effectiveness of the approach, we also conducted a study on 37,600 health-related press releases published on EurekAlert!, which showed that our approach was able to identify the corresponding research papers with a top-1 accuracy of at least 0.97.