  Twitter has been a prominent social media platform for mining
population-level health data and accurate clustering of health-related tweets
into topics is important for extracting relevant health insights. In this work,
we propose deep convolutional autoencoders for learning compact representations
of health-related tweets, further to be employed in clustering. We compare our
method to several conventional tweet representation methods including
bag-of-words, term frequency-inverse document frequency, Latent Dirichlet
Allocation and Non-negative Matrix Factorization with 3 different clustering
algorithms. Our results show that the clustering performance using proposed
representation learning scheme significantly outperforms that of conventional
methods for all experiments of different number of clusters. In addition, we
propose a constraint on the learned representations during the neural network
training in order to further enhance the clustering performance. All in all,
this study introduces utilization of deep neural network-based architectures,
i.e., deep convolutional autoencoders, for learning informative representations
of health-related tweets.
