  Topic models are widely used in studying social phenomena. We conduct a
comparative study examining state-of-the-art neural versus non-neural topic
models, performing a rigorous quantitative and qualitative assessment on a
dataset of tweets about the COVID-19 pandemic. Our results show that not only
do neural topic models outperform their classical counterparts on standard
evaluation metrics, but they also produce more coherent topics, which are of
great benefit when studying complex social problems. We also propose a novel
regularization term for neural topic models, which is designed to address the
well-documented problem of mode collapse, and demonstrate its effectiveness.
