Quantifying the online long-term interest in research

作者：

Highlights：

• We proposed and evaluated the metric “Online Age” to measure and quantify the online interest in research articles.

• We investigated the growth in online mentions of research articles on different online platforms for articles published from 1920 through 2018.

• We developed machine learning models to predict the long-term online interest in research articles and identified the most influential online sites that amplify this interest.

摘要

•We proposed and evaluated the metric “Online Age” to measure and quantify the online interest in research articles.•We investigated the growth in online mentions of research articles on different online platforms for articles published from 1920 through 2018.•We developed machine learning models to predict the long-term online interest in research articles and identified the most influential online sites that amplify this interest.

论文关键词：Long-term research interest,Online scholarly impact,Aging of articles,Social media,Altmetrics,Machine learning

论文评审过程：Received 10 April 2021, Revised 19 March 2022, Accepted 28 March 2022, Available online 11 April 2022, Version of Record 11 April 2022.

论文官网地址：https://doi.org/10.1016/j.joi.2022.101288