Abstract
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 neglecting prediction abilities 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 computational social science (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 data-driven computational social network science (DD-CSNS).
Highlights
The social sciences study the general behavior of groups, communities, and societies, and the interactions among such entities and their changes over time
We argue that the combination of big social data with social networks enables the definition of prediction models which can be utilized for solution-oriented approaches
What can be learned from sciences at lower hierarchy levels below the social sciences? Maybe the biggest leap of progress within the last few decades has been achieved in biology
Summary
The social sciences study the general behavior of groups, communities, and societies, and the interactions among such entities and their changes over time. Recent progress in information technology created new means to exchange digital information via social media, text messaging, or phone calls which led to a surge of data capturing a wealth of information about the underlying social behavior of individuals and groups This opened new possibility and challenges at the same time because the resulting big social data cannot be analyzed in a simulationbased manner as, e.g., provided by CSS. We want to emphasize that by arguing in favor of DD-CSNS, we do not imply that this renders simulation-based studies as mute or inferior but that in the light of the current big social data surge a data-driven computational social network science (DDCSNS) provides complementary qualities that deserve special attention (Chang et al, 2014)
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