Emmert-Streib Frank, Dehmer Matthias
Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
Institute of Biosciences and Medical Technology, Tampere, Finland.
Front Big Data. 2021 Apr 22;4:591749. doi: 10.3389/fdata.2021.591749. eCollection 2021.
The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach (DD-CSNS).
社会科学的最终目标是找到一种涵盖社会和集体现象所有方面的一般社会理论。传统的方法非常严格，试图寻找因果解释和模型。然而，这种方法最近受到批评，因为支持更多面向问题的方法的模型的出现阻碍了进步。后一种模型将由当前可用的大量网络数据的激增来实现。有趣的是，仅靠计算社会科学（CSS）的方法无法克服这个问题，因为该领域主要由基于模拟的方法和描述性模型主导。在本文中，我们解决这个问题，并认为创建预测模型需要将大量社会数据与社会网络相结合。我们将论证，这种联盟有潜力逐步建立一种因果社会理论。为了强调将大量社会数据与社会网络整合的重要性，我们将这种方法称为深度数据驱动的因果社会网络（DD-CSNS）。
