[Objective] This study tries to predict visibility of research papers on Twitter with their multidimensional features, aiming to find important factors affecting social media visibility. [Methods] First, we decided each paper’s social media visibility by its total mentions on Twitter, and extracted features from paper contents, authorship and publishing journals. Then, we constructed a binary classification model to predict each paper’s Twitter visibility. Finally, we examined our model with papers on diabetes to evaluate the performance of different algorithms and the importance of all features. [Results] LightGBM had the best performance with an accuracy of 0.70. Features from contents, authorship and publishing journals all influenced an article’s visibility on social media, while a journal’s annual average impact factor was the most important one. [Limitations] We only examined visiblity of diabete related papers on Twitter. [Conclusions] Ensemble learning algorithm is an effective method to predict social media visibility of scholarly articles, while features of the publishing journals are the key factors.