论文标题
通过无监督的深度学习，对社交媒体的政治倾斜的细粒度预测
Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning
论文作者
论文摘要
鉴于其对选举预测，意见动态模型以及研究两极分化和虚假信息的政治层面的有用性，预测社交媒体使用者的政治倾向是一项越来越流行的任务。在这里，我们提出了一种新颖的无监督技术，可以从社交媒体帖子的文本内容中学习细颗粒的政治倾向。我们的技术利用一个深层的神经网络在表示任务中学习潜在的政治意识形态。然后，将用户投射在低维意识形态空间中，随后将其聚类。用户的政治倾向自动从分配的群集中得出。我们在两项具有挑战性的分类任务中评估了我们的技术，并将其与基线和其他最先进的方法进行了比较。我们的技术在所有无监督的技术中都获得了最佳结果，在8级任务中，Micro F1 = 0.426，在3级任务中Micro F1 = 0.772。除了自己有趣之外，我们的结果还为开发新的，更好的无监督方法铺平了道路，以检测精美的政治倾向。
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.
