Abstract As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks.

中文翻译：

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一种基于图的半监督数字地理分类学习方法

摘要 随着在线空间和物理空间之间的区别迅速缩小，社交媒体现在已成为多少人的日常体验被中介的不可或缺的组成部分。因此，人们越来越有兴趣探索通过这些在线平台共享的内容如何在城市规模的物理空间中协同创造场所。使用定性编码（即内容标记）等方法探索社交媒体数据的数字地理是一项灵活但复杂的任务，由于其在大型数据集上的不切实际，通常仅限于小样本。在本文中，我们提出了一种用于研究数字地理学的新工具，连接定性和定量方法，能够在一个小的、手动创建的样本，并在更大的集合上应用相同的标签。我们引入了一种半监督的深度神经网络方法，根据文本和图像内容以及地理和时间方面对地理定位的社交媒体帖子进行分类。我们的创新方法植根于我们将社交媒体帖子理解为场所时空配置的增强，它包括一个堆叠的多模态自动编码器神经网络，用于创建文本和图像的联合表示，以及一个时空图用于半监督分类的卷积神经网络。本文中提出的结果表明，我们的方法对社交媒体内容进行分类的准确度高于传统机器学习模型以及两个最先进的深度学习框架。