# Quantifying social organization and political polarization in online platforms

arXiv: https://arxiv.org/abs/2010.00590 · HTML: https://mlsanity.com/paper/2010.00590/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 30 of 30)

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- [Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning](https://mlsanity.com/paper/2209.09803/) (2022)
- [Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context](https://mlsanity.com/paper/2301.04248/) (2023)
- [The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models](https://mlsanity.com/paper/2507.17216/) (2025)
- [Understanding Online Migration Decisions Following the Banning of Radical Communities](https://mlsanity.com/paper/2212.04765/) (2022)
- [Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media](https://mlsanity.com/paper/2307.09312/) (2023)
- [Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus](https://mlsanity.com/paper/2411.07892/) (2024)
- [Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit](https://mlsanity.com/paper/2310.12186/) (2023)
- [Computational Assessment of Hyperpartisanship in News Titles](https://mlsanity.com/paper/2301.06270/) (2023)
- [POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization](https://mlsanity.com/paper/2505.20624/) (2025)
- [Word Embedding for Social Sciences: An Interdisciplinary Survey](https://mlsanity.com/paper/2207.03086/) (2022)
- [Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums](https://mlsanity.com/paper/2407.03551/) (2024)
- [United States Politicians' Tone Became More Negative with 2016 Primary Campaigns](https://mlsanity.com/paper/2207.08112/) (2022)
- [Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content](https://mlsanity.com/paper/2301.10871/) (2023)
- [Neural network embeddings recover value dimensions from psychometric survey items on par with human data](https://mlsanity.com/paper/2509.24906/) (2025)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [Tracking the Takes and Trajectories of English-Language News Narratives across Trustworthy and Worrisome Websites](https://mlsanity.com/paper/2501.09102/) (2025)
- [Gendered Pathways in AI Companionship: Cross-Community Behavior and Toxicity Patterns on Reddit](https://mlsanity.com/paper/2601.01073/) (2026)
- [SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization](https://mlsanity.com/paper/2604.06817/) (2026)
- [Characterizing Human Actions in the Digital Platform by Temporal Context](https://mlsanity.com/paper/2206.09535/) (2022)
- [Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization](https://mlsanity.com/paper/2209.04329/) (2022)
- [Inference of Media Bias and Content Quality Using Natural-Language Processing](https://mlsanity.com/paper/2212.00237/) (2022)
- [Moral consensus and divergence in partisan language use](https://mlsanity.com/paper/2310.09618/) (2023)
- [SoMeR: Multi-View User Representation Learning for Social Media](https://mlsanity.com/paper/2405.05275/) (2024)
- [AI-Mediated Communication Reshapes Social Structure in Opinion-Diverse Groups](https://mlsanity.com/paper/2510.21984/) (2025)
- [GASTON: Graph-Aware Social Transformer for Online Networks](https://mlsanity.com/paper/2602.02524/) (2026)
- [Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media](https://mlsanity.com/paper/2602.02525/) (2026)
- [Quantifying Political Partisanship for Cross-Platform Analyses](https://mlsanity.com/paper/2607.21842/) (2026)

## Abstract (arXiv)

Optimism about the Internet's potential to bring the world together has been tempered by concerns about its role in inflaming the 'culture wars'. Via mass selection into like-minded groups, online society may be becoming more fragmented and polarized, particularly with respect to partisan differences. However, our ability to measure the social makeup of online communities, and in turn understand the social organization of online platforms, is limited by the pseudonymous, unstructured, and large-scale nature of digital discussion. We develop a neural embedding methodology to quantify the positioning of online communities along social dimensions by leveraging large-scale patterns of aggregate behaviour. Applying our methodology to 5.1B Reddit comments made in 10K communities over 14 years, we measure how the macroscale community structure is organized with respect to age, gender, and U.S. political partisanship. Examining political content, we find Reddit underwent a significant polarization event around the 2016 U.S. presidential election, and remained highly polarized for years afterward. Contrary to conventional wisdom, however, individual-level polarization is rare; the system-level shift in 2016 was disproportionately driven by the arrival of new and newly political users. Political polarization on Reddit is unrelated to previous activity on the platform, and is instead temporally aligned with external events. We also observe a stark ideological asymmetry, with the sharp increase in 2016 being entirely attributable to changes in right-wing activity. Our methodology is broadly applicable to the study of online interaction, and our findings have implications for the design of online platforms, understanding the social contexts of online behaviour, and quantifying the dynamics and mechanisms of online polarization.