# The Branch Not Taken: Predicting Branching in Online Conversations

arXiv: https://arxiv.org/abs/2404.13613 · HTML: https://mlsanity.com/paper/2404.13613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 21 Apr 2024.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Conversations Gone Awry: Detecting Early Signs of Conversational Failure](https://mlsanity.com/paper/1805.05345/) (2018)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [The effect of wording on message propagation: Topic- and author-controlled natural experiments on Twitter](https://mlsanity.com/paper/1405.1438/) (2014)
- [Who did They Respond to? Conversation Structure Modeling using Masked Hierarchical Transformer](https://mlsanity.com/paper/1911.10666/) (2019)
- [Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity](https://mlsanity.com/paper/1909.08211/) (2019)
- [Conversation Modeling on Reddit using a Graph-Structured LSTM](https://mlsanity.com/paper/1704.02080/) (2017)
- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Identifying the social signals that drive online discussions: A case study of Reddit communities](https://mlsanity.com/paper/1705.02673/) (2017)
- [Discourse Parsing of Contentious, Non-Convergent Online Discussions](https://mlsanity.com/paper/2012.04585/) (2020)

## Built on by (most cited first, 1 of 1)

- [Fast Response or Silence: Conversation Persistence in an AI-Agent Social Network](https://mlsanity.com/paper/2602.07667/) (2026)

## Abstract (arXiv)

Multi-participant discussions tend to unfold in a tree structure rather than a chain structure. Branching may occur for multiple reasons -- from the asynchronous nature of online platforms to a conscious decision by an interlocutor to disengage with part of the conversation. Predicting branching and understanding the reasons for creating new branches is important for many downstream tasks such as summarization and thread disentanglement and may help develop online spaces that encourage users to engage in online discussions in more meaningful ways. In this work, we define the novel task of branch prediction and propose GLOBS (Global Branching Score) -- a deep neural network model for predicting branching. GLOBS is evaluated on three large discussion forums from Reddit, achieving significant improvements over an array of competitive baselines and demonstrating better transferability. We affirm that structural, temporal, and linguistic features contribute to GLOBS success and find that branching is associated with a greater number of conversation participants and tends to occur in earlier levels of the conversation tree. We publicly release GLOBS and our implementation of all baseline models to allow reproducibility and promote further research on this important task.