Quantifying the Online Long-Term Interest in Research

TL;DR

This study analyzes online sharing patterns of research articles across platforms, categorizes articles by online mention longevity, and develops machine learning models to predict long-term online interest, revealing differences based on article age and field.

Contribution

The paper introduces a method to quantify long-term online interest in research articles using social media metrics and machine learning, with novel clustering and prediction approaches.

Findings

Old articles are prominent in economics and patents.

Recent articles are more discussed on Mendeley and Twitter.

ML models effectively predict long-term online interest.

Abstract

Research articles are being shared in increasing numbers on multiple online platforms. Although the scholarly impact of these articles has been widely studied, the online interest determined by how long the research articles are shared online remains unclear. Being cognizant of how long a research article is mentioned online could be valuable information to the researchers. In this paper, we analyzed multiple social media platforms on which users share and/or discuss scholarly articles. We built three clusters for papers, based on the number of yearly online mentions having publication dates ranging from the year 1920 to 2016. Using the online social media metrics for each of these three clusters, we built machine learning models to predict the long-term online interest in research articles. We addressed the prediction task with two different approaches: regression and classification. For the regression approach, the Multi-Layer Perceptron model performed best, and for the classification approach, the tree-based models performed better than other models. We found that old articles are most evident in the contexts of economics and industry (i.e., patents). In contrast, recently published articles are most evident in research platforms (i.e., Mendeley) followed by social media platforms (i.e., Twitter).