# Predicting Political Ideology from Digital Footprints

arXiv: https://arxiv.org/abs/2206.00397 · HTML: https://mlsanity.com/paper/2206.00397/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 68 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations](https://mlsanity.com/paper/1701.05632/) (2017)

## Built on by (most cited first, 2 of 2)

- [Inference of Media Bias and Content Quality Using Natural-Language Processing](https://mlsanity.com/paper/2212.00237/) (2022)
- [Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias](https://mlsanity.com/paper/2309.05589/) (2023)

## Abstract (arXiv)

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.