Learning Ideological Embeddings from Information Cascades

TL;DR

This paper introduces a stochastic model that learns users' ideological positions from information cascades on social media, aiding understanding of misinformation spread and bias.

Contribution

It presents a novel multidimensional ideological embedding model and a gradient-based inference method for analyzing political content propagation.

Findings

Model accurately infers users' political stances

Effective on real-world Twitter and Reddit data

Enhances understanding of ideological influence in social networks

Abstract

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.