# A Long-Term Analysis of Polarization on Twitter

arXiv: https://arxiv.org/abs/1703.02769 · HTML: https://mlsanity.com/paper/1703.02769/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2017.

## Built on by (most cited first, 17 of 17)

- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [LLMs generate structurally realistic social networks but overestimate political homophily](https://mlsanity.com/paper/2408.16629/) (2024)
- [POLE: Polarized Embedding for Signed Networks](https://mlsanity.com/paper/2110.09899/) (2021)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model](https://mlsanity.com/paper/2301.09507/) (2023)
- [Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study](https://mlsanity.com/paper/2112.04913/) (2021)
- [Hate versus Politics: Detection of Hate against Policy makers in Italian tweets](https://mlsanity.com/paper/2107.05357/) (2021)
- [An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://mlsanity.com/paper/2602.03775/) (2026)
- [Adversaries with Limited Information in the Friedkin--Johnsen Model](https://mlsanity.com/paper/2306.10313/) (2023)
- [Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction](https://mlsanity.com/paper/2603.23531/) (2026)
- [Party Prediction for Twitter](https://mlsanity.com/paper/2308.13699/) (2023)
- [Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias](https://mlsanity.com/paper/2309.05589/) (2023)
- [Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization](https://mlsanity.com/paper/2402.11895/) (2024)
- [Political Leaning Inference through Plurinational Scenarios](https://mlsanity.com/paper/2406.07964/) (2024)
- [How Similar Are Elected Politicians and Their Constituents? Quantitative Evidence From Online Social Networks](https://mlsanity.com/paper/2407.03255/) (2024)
- [Visibility Allocation Systems: How Algorithmic Design Shapes Online Visibility and Societal Outcomes](https://mlsanity.com/paper/2510.17241/) (2025)

## Abstract (arXiv)

Social media has played an important role in shaping political discourse over the last decade. At the same time, it is often perceived to have increased political polarization, thanks to the scale of discussions and their public nature. In this paper, we try to answer the question of whether political polarization in the US on Twitter has increased over the last eight years. We analyze a large longitudinal Twitter dataset of 679,000 users and look at signs of polarization in their (i) network - how people follow political and media accounts, (ii) tweeting behavior - whether they retweet content from both sides, and (iii) content - how partisan the hashtags they use are. Our analysis shows that online polarization has indeed increased over the past eight years and that, depending on the measure, the relative change is 10%-20%. Our study is one of very few with such a long-term perspective, encompassing two US presidential elections and two mid-term elections, providing a rare longitudinal analysis.
