Whats next? Forecasting scientific research trends

TL;DR

This paper develops a forecasting method for scientific research trends using diverse data sources and language models, enabling predictions of topic popularity five years ahead across multiple scientific domains.

Contribution

It introduces a novel approach combining heterogeneous data and language models to predict future scientific research trends with high accuracy.

Findings

Models outperform historical baselines in trend prediction.

Preceding publications and patents are key indicators.

The ratio of reviews to original articles signals trend changes.

Abstract

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, research and review articles, pre-trained language models, and patents. We demonstrate that scientific topic popularity levels and changes (trends) can be predicted five years in advance across 40 years and 125 diverse topics, including life-science concepts, biomedical, anatomy, and other science, technology, and engineering topics. Preceding publications and future patents are leading indicators for emerging scientific topics. We find the ratio of reviews to original research articles informative for identifying increasing or declining topics, with declining topics having an excess of reviews. We find that language models provide improved insights and predictions into temporal dynamics. In temporal validation, our models substantially outperform the historical baseline. Our findings suggest that similar dynamics apply across other scientific and engineering research topics. We present SciTrends, a user-friendly webtool for predicting scientific topics trends: https://hadasakaufman.shinyapps.io/SciTrend