  In this paper, we investigate the effectiveness of unsupervised feature
learning techniques in predicting user engagement on social media.
Specifically, we compare two methods to predict the number of feedbacks (i.e.,
comments) that a blog post is likely to receive. We compare Principal Component
Analysis (PCA) and sparse Autoencoder to a baseline method where the data are
only centered and scaled, on each of two models: Linear Regression and
Regression Tree. We find that unsupervised learning techniques significantly
improve the prediction accuracy on both models. For the Linear Regression
model, sparse Autoencoder achieves the best result, with an improvement in the
root mean squared error (RMSE) on the test set of 42% over the baseline method.
For the Regression Tree model, PCA achieves the best result, with an
improvement in RMSE of 15% over the baseline.
