To follow or not to follow: estimating political opinion from Twitter data using a network-based machine learning approach
Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studie...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
September 3, 2024
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| In: |
Social science computer review
Year: 2024, Pages: 1-22 |
| ISSN: | 1552-8286 |
| DOI: | 10.1177/08944393241279418 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1177/08944393241279418
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| Verfasserangaben: | Nils Brandenstein, Christian Montag, and Cornelia Sindermann |
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| 520 | |a Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts. | ||
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