# Loyalty in Online Communities

arXiv: https://arxiv.org/abs/1703.03386 · HTML: https://mlsanity.com/paper/1703.03386/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

## Built on by (most cited first, 12 of 12)

- [Characterizing English Variation across Social Media Communities with BERT](https://mlsanity.com/paper/2102.06820/) (2021)
- [MentorGNN: Deriving Curriculum for Pre-Training GNNs](https://mlsanity.com/paper/2208.09905/) (2022)
- [The structure of online social networks modulates the rate of lexical change](https://mlsanity.com/paper/2104.05010/) (2021)
- [Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions](https://mlsanity.com/paper/2204.10729/) (2022)
- [Deeper-GXX: Deepening Arbitrary GNNs](https://mlsanity.com/paper/2110.13798/) (2021)
- [Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender](https://mlsanity.com/paper/2109.11061/) (2021)
- [Social Simulacra in the Wild: AI Agent Communities on Moltbook](https://mlsanity.com/paper/2603.16128/) (2026)
- [With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns](https://mlsanity.com/paper/2307.09650/) (2023)
- [Room to Grow: Understanding Personal Characteristics Behind Self Improvement Using Social Media](https://mlsanity.com/paper/2105.08031/) (2021)
- [This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign](https://mlsanity.com/paper/2201.05334/) (2022)
- [We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience](https://mlsanity.com/paper/2208.10766/) (2022)
- [gSuite: A Flexible and Framework Independent Benchmark Suite for Graph Neural Network Inference on GPUs](https://mlsanity.com/paper/2210.11601/) (2022)

## Abstract (arXiv)

Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether loyalty is encouraged by certain community characteristics. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring this relation using a large dataset of discussion communities from Reddit, we reveal that loyalty is manifested in remarkably consistent behaviors across a wide spectrum of communities. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user's propensity to become loyal is apparent from their first interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.