# Measuring Political Polarization: Twitter shows the two sides of Venezuela

arXiv: https://arxiv.org/abs/1505.04095 · HTML: https://mlsanity.com/paper/1505.04095/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2015.

## Built on by (most cited first, 7 of 7)

- [A Survey on Echo Chambers on Social Media: Description, Detection and Mitigation](https://mlsanity.com/paper/2112.05084/) (2021)
- [Controversy Detection: a Text and Graph Neural Network Based Approach](https://mlsanity.com/paper/2112.11445/) (2021)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [Language statistics at different spatial, temporal, and grammatical scales](https://mlsanity.com/paper/2207.00709/) (2022)
- [All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media](https://mlsanity.com/paper/2312.04603/) (2023)
- [Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization](https://mlsanity.com/paper/2402.11895/) (2024)
- [Quantifying Political Partisanship for Cross-Platform Analyses](https://mlsanity.com/paper/2607.21842/) (2026)

## Abstract (arXiv)

We say that a population is perfectly polarized when divided in two groups of the same size and opposite opinions. In this paper, we propose a methodology to study and measure the emergence of polarization from social interactions. We begin by proposing a model to estimate opinions in which a minority of influential individuals propagate their opinions through a social network. The result of the model is an opinion probability density function. Next, we propose an index to quantify the extent to which the resulting distribution is polarized. Finally, we apply the proposed methodology to a Twitter conversation about the late Venezuelan president, Hugo Ch'avez, finding a good agreement between our results and offline data. Hence, we show that our methodology can detect different degrees of polarization, depending on the structure of the network.