# What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations

arXiv: https://arxiv.org/abs/1903.07319 · HTML: https://mlsanity.com/paper/1903.07319/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://mlsanity.com/paper/2106.09896/) (2021)
- [Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure](https://mlsanity.com/paper/2104.08601/) (2021)
- [Inducing Causal Structure for Abstractive Text Summarization](https://mlsanity.com/paper/2308.12888/) (2023)
- [Improving Topic Modeling by Distilling Soft Labels from Language Models](https://mlsanity.com/paper/2602.17907/) (2026)

## Abstract (arXiv)

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier.