Opinion formation and propagation are crucial phenomena in social networks and have been extensively studied across several disciplines. Traditionally, theoretical models of opinion dynamics have been proposed to describe the interactions between individuals (i.e., social interaction) and their impact on the evolution of collective opinions. Although these models can incorporate sociological and psychological knowledge on the mechanisms of social interaction, they demand extensive calibration with real data to make reliable predictions, requiring much time and effort. Recently, the widespread use of social media platforms provides new paradigms to learn deep learning models from a large volume of social media data. However, these methods ignore any scientific knowledge about the mechanism of social interaction. In this work, we present the first hybrid method called Sociologically-Informed Neural Network (SINN), which integrates theoretical models and social media data by transporting the concepts of physics-informed neural networks (PINNs) from natural science (i.e., physics) into social science (i.e., sociology and social psychology). In particular, we recast theoretical models as ordinary differential equations (ODEs). Then we train a neural network that simultaneously approximates the data and conforms to the ODEs that represent the social scientific knowledge. In addition, we extend PINNs by integrating matrix factorization and a language model to incorporate rich side information (e.g., user profiles) and structural knowledge (e.g., cluster structure of the social interaction network). Moreover, we develop an end-to-end training procedure for SINN, which involves Gumbel-Softmax approximation to include stochastic mechanisms of social interaction. Extensive experiments on real-world and synthetic datasets show SINN outperforms six baseline methods in predicting opinion dynamics.
翻译:观点的形成和传播是社会网络中至关重要的现象,而且已在多个学科中进行了广泛研究。传统上,提出了理论观点动态模型,以描述个人之间的相互作用(即社会互动)及其对集体观点演变的影响。虽然这些模型可以将社会互动机制的社会学和心理知识纳入社会互动机制,但它们要求用真实数据进行广泛的校准,以作出可靠的预测,需要花费大量的时间和精力。最近,广泛使用社交媒体平台提供了从大量社交媒体数据中学习深层次学习模型的新范例。然而,这些互动方法忽视了社会互动机制的任何科学知识。在此工作中,我们介绍了第一种混合方法,即社会学化神经网络(SINNN),该方法将理论模型和社会媒体数据数据整合到社会互动机制(即社会学和社会心理学)的概念中。我们重新将理论模型模型作为普通的智能化公式(ODs)。然后,我们培训一个神经网络,即社会智能网络(SONIS),将我们的社会信息引入了一个社会学要素。我们通过社会学模型、社会学、社会学模型和数学模型展示了一个社会学模型,我们将一个社会学模型显示一个社会学的模型。