Characterizing Online Discussion Using Coarse Discourse Sequences

By Amy X. Zhang, Bryan Culbertson, Praveen K. Paritosh. 2017

104 citations recorded by the source corpus.

In this work, we present a novel method for classifying comments in online discussions into a set of coarse discourse acts towards the goal of better understanding discussions at scale. To facilitate this study, we devise a categorization of coarse discourse acts designed to encompass general online discussion and allow for easy annotation by crowd workers. We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit. Using our corpus, we demonstrate how the analysis of discourse acts can characterize different types of discussions, including discourse sequences such as Q&A pairs and chains of disagreement, as well as different communities. Finally, we conduct experiments to predict discourse acts using our corpus, finding that structured prediction models such as conditional random fields can achieve an F1 score of 75%. We also demonstrate how the broadening of discourse acts from simply question and answer to a richer set of categories can improve the recall performance of Q&A extraction.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Natural Language Processing, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Diyi Yang
2. Jiaao Chen
3. Omar Shaikh
4. Caleb Ziems
5. R. Chulaka Gunasekara
6. Lazaros C. Polymenakos
7. Zhehao Zhang
8. Walter S. Lasecki

In Computer Science Applications

1. Omar Shaikh
2. Caleb Ziems
3. Diyi Yang

In Information Systems

1. Caleb Ziems
2. Lazaros C. Polymenakos
3. Diyi Yang
4. Walter S. Lasecki

In Natural Language Processing

1. Diyi Yang
2. Jiaao Chen
3. Omar Shaikh
4. Caleb Ziems
5. Lazaros C. Polymenakos
6. Zhehao Zhang
7. Walter S. Lasecki
8. William B. Held

Citations in the corpus

• Analyzing Assumptions in Conversation Disentanglement Research Through the Lens of a New Dataset and Model (2018, background): “They may be useful for partial supervision, but only a few publications have released their data (Abbott et al., 2016; Zhang et al., 2017; Louis and Cohen, 2015).”
• Can Large Language Models Transform Computational Social Science? (2023, methodology): “We use the simpler and more focused 9-class taxonomy of Zhang et al. (2017) since it was designed to cover online text conversations—the focus of CSS research.”