Mass. Curtails Free Vaccines for Older Children

Anderson, Jane Collier

doi:10.1016/s0031-398x(08)70168-1

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a much-noticed article by Anderson (2008), it has been somewhat gloomily argued that "the data deluge makes the scientific method obsolete." In contrast, in this study, we brought forward the view of DD-CSNS as a data science.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

Emmert‐Streib¹,

Dehmer²

2021

Front. Big Data

View full text Add to dashboard Cite

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).

show abstract

Section: Discussionmentioning

confidence: 99%