Predicting the future success of scientific publications through social network and semantic analysis

TL;DR

This study predicts the future citation impact of scientific papers using social network and semantic analysis, achieving nearly 80% accuracy by analyzing author collaborations and abstract language features.

Contribution

It introduces an empirical approach combining social network analysis and text mining to forecast scholarly impact, controlling for intrinsic quality and author count.

Findings

Co-publishing with rotating co-authors increases impact.

Using positive words and complex language in abstracts correlates with higher citations.

Collaborations across different social groups attract more citations.

Abstract

Citations acknowledge the impact a scientific publication has on subsequent work. At the same time, deciding how and when to cite a paper, is also heavily influenced by social factors. In this work, we conduct an empirical analysis based on a dataset of 2010-2012 global publications in chemical engineering. We use social network analysis and text mining to measure publication attributes and understand which variables can better help predicting their future success. Controlling for intrinsic quality of a publication and for the number of authors in the byline, we are able to predict scholarly impact of a paper in terms of citations received six years after publication with almost 80 percent accuracy. Results suggest that, all other things being equal, it is better to co-publish with rotating co-authors and write the papers' abstract using more positive words, and a more complex, thus more informative, language. Publications that result from the collaboration of different social groups also attract more citations.