# Identifying the social signals that drive online discussions: A case study of Reddit communities

arXiv: https://arxiv.org/abs/1705.02673 · HTML: https://mlsanity.com/paper/1705.02673/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2017.

## Built on by (most cited first, 6 of 6)

- [Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions](https://mlsanity.com/paper/2204.10729/) (2022)
- [The Branch Not Taken: Predicting Branching in Online Conversations](https://mlsanity.com/paper/2404.13613/) (2024)
- [Predicting Evoked Emotions in Conversations](https://mlsanity.com/paper/2401.00383/) (2023)
- [Causal Effects of Trigger Words in Social Media Discussions: A Large-Scale Case Study about UK Politics on Reddit](https://mlsanity.com/paper/2405.10213/) (2024)
- [The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing](https://mlsanity.com/paper/2505.03769/) (2025)
- [Density-Guided Response Optimization: Community-Grounded Alignment via Implicit Acceptance Signals](https://mlsanity.com/paper/2603.03242/) (2026)

## Abstract (arXiv)

Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we focus on online discussions. Can we predict which comments and what content gets the highest attention in an online discussion? How does this content differ from community to community? To accomplish this, we undertake a unique study of Reddit involving a large sample comments from 11 popular subreddits with different properties. We introduce a large number of sentiment, relevance, content analysis features including some novel features customized to reddit. Through a comparative analysis of the chosen subreddits, we show that our models are correctly able to retrieve top replies under a post with great precision. In addition, we explain our findings with a detailed analysis of what distinguishes high scoring posts in different communities that differ along the dimensions of the specificity of topic and style, audience and level of moderation.