# Learning Ideological Embeddings from Information Cascades

arXiv: https://arxiv.org/abs/2109.13589 · HTML: https://mlsanity.com/paper/2109.13589/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Stochastic Blockmodels meet Graph Neural Networks](https://mlsanity.com/paper/1905.05738/) (2019)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016)
- [Learning Opinion Dynamics From Social Traces](https://mlsanity.com/paper/2006.01673/) (2020)
- [Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter](https://mlsanity.com/paper/2003.11906/) (2020)
- [Modeling confirmation bias and polarization](https://mlsanity.com/paper/1607.00022/) (2016)
- [Polarization of the Vaccination Debate on Facebook](https://mlsanity.com/paper/1801.02903/) (2018)
- [Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention](https://mlsanity.com/paper/1801.03032/) (2018)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [Balancing Information Exposure in Social Networks](https://mlsanity.com/paper/1709.01491/) (2017)
- [Finding Interpretable Concept Spaces in Node Embeddings using Knowledge Bases](https://mlsanity.com/paper/1910.05030/) (2019)
- [Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit](https://mlsanity.com/paper/2005.01790/) (2020)
- [Viral Marketing Meets Social Advertising: Ad Allocation with Minimum Regret](https://mlsanity.com/paper/1412.1462/) (2014)
- [Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter](https://mlsanity.com/paper/1711.10251/) (2017)
- [Maximizing the Diversity of Exposure in a Social Network](https://mlsanity.com/paper/1809.04393/) (2018)
- [tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification](https://mlsanity.com/paper/1902.00438/) (2019)
- [Revenue Maximization in Incentivized Social Advertising](https://mlsanity.com/paper/1612.00531/) (2016)

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

- [Learning Stance Embeddings from Signed Social Graphs](https://mlsanity.com/paper/2201.11675/) (2022)
- [Cascade-based Echo Chamber Detection](https://mlsanity.com/paper/2208.04620/) (2022)
- [Engagement-Driven Content Generation with Large Language Models](https://mlsanity.com/paper/2411.13187/) (2024)
- [Empowering Interdisciplinary Insights with Dynamic Graph Embedding Trajectories](https://mlsanity.com/paper/2406.17963/) (2024)
- [Machines & Influence: An Information Systems Lens](https://mlsanity.com/paper/2111.13365/) (2021)
- [Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies](https://mlsanity.com/paper/2601.07884/) (2026)

## Abstract (arXiv)

Modeling information cascades in a social network through the lenses of the ideological leaning of its users can help understanding phenomena such as misinformation propagation and confirmation bias, and devising techniques for mitigating their toxic effects. In this paper we propose a stochastic model to learn the ideological leaning of each user in a multidimensional ideological space, by analyzing the way politically salient content propagates. In particular, our model assumes that information propagates from one user to another if both users are interested in the topic and ideologically aligned with each other. To infer the parameters of our model, we devise a gradient-based optimization procedure maximizing the likelihood of an observed set of information cascades. Our experiments on real-world political discussions on Twitter and Reddit confirm that our model is able to learn the political stance of the social media users in a multidimensional ideological space.