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).

该研究利用机器学习模型分析了多个社交媒体平台上文章被在线分享的持续时间，以预测研究文章的长期在线兴趣。研究发现，旧的文章主要与经济和工业相关，而最近发表的文章则最多在研究平台和社交媒体平台方面被提及。