# Find the Conversation Killers: a Predictive Study of Thread-ending Posts

arXiv: https://arxiv.org/abs/1712.08636 · HTML: https://mlsanity.com/paper/1712.08636/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations](https://mlsanity.com/paper/2102.08368/) (2021)
- [Echoes of Discord: Forecasting Hater Reactions to Counterspeech](https://mlsanity.com/paper/2501.16235/) (2025)
- [Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure](https://mlsanity.com/paper/2104.08601/) (2021)
- [Leveraging Large Language Models to Identify Conversation Threads in Collaborative Learning](https://mlsanity.com/paper/2510.22844/) (2025)

## Abstract (arXiv)

How to improve the quality of conversations in online communities has attracted considerable attention recently. Having engaged, urbane, and reactive online conversations has a critical effect on the social life of Internet users. In this study, we are particularly interested in identifying a post in a multi-party conversation that is unlikely to be further replied to, which therefore kills that thread of the conversation. For this purpose, we propose a deep learning model called the ConverNet. ConverNet is attractive due to its capability of modeling the internal structure of a long conversation and its appropriate encoding of the contextual information of the conversation, through effective integration of attention mechanisms. Empirical experiments on real-world datasets demonstrate the effectiveness of the proposal model. For the widely concerned topic, our analysis also offers implications for improving the quality and user experience of online conversations.