Predicting Political Ideology from Digital Footprints

@article{Kitchener2022PredictingPI,
  title={Predicting Political Ideology from Digital Footprints},
  author={Michael Kitchener and Nandini Anantharama and S. Angus and Paul A. Raschky},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.00397}
}
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… 

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References

SHOWING 1-10 OF 74 REFERENCES

Predicting the Political Alignment of Twitter Users

Several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections are described and a practical application of this machinery to web-based political advertising is outlined.

Beyond Binary Labels: Political Ideology Prediction of Twitter Users

This study examines users’ political ideology using a seven-point scale which enables it to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters.

Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data

Politicians and citizens increasingly engage in political conversations on social media outlets such as Twitter. In this article, I show that the structure of the social networks in which they are

Predicting political preference of Twitter users

This work builds prediction models based on a variety of contextual and behavioral features, training the models by resorting to a distant supervision approach and considering party candidates to have a predefined preference towards their respective parties, and uses the model to analyze the preference changes over the course of the election campaign.

Predicting Personality from Twitter

This paper presents a method by which a user's personality can be accurately predicted through the publicly available information on their Twitter profile, and the implications this has for social media design, interface design, and broader domains.

Predicting Individual Characteristics from Digital Traces on Social Media: A Meta-Analysis

It is indicated that digital traces from social media can be studied to assess and predict theoretically distant psychosocial characteristics with remarkable accuracy.

What's in Your Tweets? I Know Who You Supported in the UK 2010 General Election

The experimental results showed that the best-performing classification method -- which uses the number of Twitter messages referring to a particular political party -- achieved about 86% classification accuracy without any training phase.

What demographic attributes do our digital footprints reveal? A systematic review

A systematic review that synthesises current evidence on predicting demographic attributes from online digital traces and provides a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied.

Predicting personality with social media

This paper presents a method by which a user's personality can be accurately predicted through the publicly available information on their Facebook profile, and the implications this has for social media design, interface design, and broader domains.
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