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.

中文翻译：

---

关注或不关注：使用基于网络的机器学习方法从 Twitter 数据中估计政治观点


研究公民的政治观点是政策制定者和研究人员的基本追求。虽然传统调查仍然是调查个人政治观点的主要方法，但社交媒体数据 (SMD) 的出现提供了新的前景。然而，使用 SMD 提取个人政治观点的研究数量有限，并且其方法论和成功程度差异很大。最近的研究强调了分析个人社交媒体网络结构来评估政治观点的好处。然而，当前的方法存在局限性，包括使用简单化的线性模型以及主要关注来自美国的样本。为了解决这些问题，我们采用无监督变分自动编码器 (VAE) 机器学习模型从 N = 276 008 德国 Twitter（现在称为“X”）用户的 SMD 中提取个人意见估计，将其性能与线性模型进行比较并验证模型估计关于自我报告意见措施。我们的研究结果表明，VAE 更准确地捕捉 Twitter 用户的网络结构，从而提高后续决策预测的准确性以及与自我报告的政治意识形态和投票意图的关联。我们的研究强调，在研究政治观点时，至少在非美国背景下，需要能够捕捉社交媒体网络中复杂关系的先进分析方法。