Unsupervised Learning For Effective User Engagement on Social Media
This work addresses improving engagement prediction for social media platforms, but it is incremental as it applies existing unsupervised methods to a specific domain.
The paper tackles predicting user engagement on social media by comparing unsupervised feature learning techniques (PCA and sparse Autoencoder) against a baseline for predicting blog post comments. Results show sparse Autoencoder improves RMSE by 42% for Linear Regression and PCA improves RMSE by 15% for Regression Tree over the baseline.
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.