Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

TL;DR

This paper introduces an unsupervised deep learning method to predict fine-grained political leanings of social media users by embedding their textual content into a latent ideology space and clustering them.

Contribution

It presents a novel unsupervised approach that learns latent political ideologies from social media text and automatically derives user political leanings, outperforming existing methods.

Findings

Achieved best results among unsupervised techniques with micro F1 scores of 0.426 and 0.772 in 8-class and 3-class tasks.

Demonstrated the effectiveness of latent ideology space for fine-grained political leaning prediction.

Paves the way for improved unsupervised methods in political ideology detection.

Abstract

Predicting the political leaning of social media users is an increasingly popular task, given its usefulness for electoral forecasts, opinion dynamics models and for studying the political dimension of polarization and disinformation. Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts. Our technique leverages a deep neural network for learning latent political ideologies in a representation learning task. Then, users are projected in a low-dimensional ideology space where they are subsequently clustered. The political leaning of a user is automatically derived from the cluster to which the user is assigned. We evaluated our technique in two challenging classification tasks and we compared it to baselines and other state-of-the-art approaches. Our technique obtains the best results among all unsupervised techniques, with micro F1 = 0.426 in the 8-class task and micro F1 = 0.772 in the 3-class task. Other than being interesting on their own, our results also pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.