# Re-entry Prediction for Online Conversations via Self-Supervised Learning

arXiv: https://arxiv.org/abs/2109.02020 · HTML: https://mlsanity.com/paper/2109.02020/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 5 Sep 2021.

## 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)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues](https://mlsanity.com/paper/2009.06265/) (2020)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks](https://mlsanity.com/paper/2007.00800/) (2020)
- [Self-Supervised Dialogue Learning](https://mlsanity.com/paper/1907.00448/) (2019)
- [Self-Supervised Learning for Contextualized Extractive Summarization](https://mlsanity.com/paper/1906.04466/) (2019)
- [Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations](https://mlsanity.com/paper/2106.09896/) (2021)
- [Joint Effects of Context and User History for Predicting Online Conversation Re-entries](https://mlsanity.com/paper/1906.01185/) (2019)
- [Characterizing and curating conversation threads: Expansion, focus, volume, re-entry](https://mlsanity.com/paper/1304.4602/) (2013)

## Built on by (most cited first, 2 of 2)

- [Outcome-Constrained Large Language Models for Countering Hate Speech](https://mlsanity.com/paper/2403.17146/) (2024)
- [Echoes of Discord: Forecasting Hater Reactions to Counterspeech](https://mlsanity.com/paper/2501.16235/) (2025)

## Abstract (arXiv)

In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the discussions which they wish to continue. Nevertheless, existing works only focus on exploiting chatting history and context information, and ignore the potential useful learning signals underlying conversation data, such as conversation thread patterns and repeated engagement of target users, which help better understand the behavior of target users in conversations. In this paper, we propose three interesting and well-founded auxiliary tasks, namely, Spread Pattern, Repeated Target user, and Turn Authorship, as the self-supervised signals for re-entry prediction. These auxiliary tasks are trained together with the main task in a multi-task manner. Experimental results on two datasets newly collected from Twitter and Reddit show that our method outperforms the previous state-of-the-arts with fewer parameters and faster convergence. Extensive experiments and analysis show the effectiveness of our proposed models and also point out some key ideas in designing self-supervised tasks.