"Big Data" : big gaps of knowledge in the field of internet science
"Big Data" : big gaps of knowledge in the field of internet science
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发表时间：
2012
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通讯作者：
Chris J. Snijders;U. Matzat;Ulf-Dietrich Reips
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作者：
Chris J. Snijders;U. Matzat;Ulf-Dietrich Reips
对所谓的“大数据”的研究已经获得了相当大的动力，预计在未来会有所增长。一个非常有趣的大数据研究流分析在线网络。许多在线网络都具有一些典型的宏观特征，如“小世界”属性。我们对导致这些特性的潜在微观过程知之甚少。大数据研究人员使用的模型通常受到数学易于解释的启发。我们建议遵循另外一种不同的策略，导致与实际在线行为相匹配的微过程的知识。然后，这些知识可以用于选择在线网络形成和演化的易于处理的模型。从社会和行为研究的洞察力是必要的，以追求这种战略的知识生成的微观过程。因此，我们的建议指出了社会科学家在大数据研究中可以发挥的独特作用。
Research on so-called ‘Big Data’ has received a considerable momentum and is expected to grow in the future. One very interesting stream of research on Big Data analyzes online networks. Many online networks are known to have some typical macro-characteristics, such as ‘small world’ properties. Much less is known about underlying micro-processes leading to these properties. The models used by Big Data researchers usually are inspired by mathematical ease of exposition. We propose to follow in addition a different strategy that leads to knowledge about micro-processes that match with actual online behavior. This knowledge can then be used for the selection of mathematically-tractable models of online network formation and evolution. Insight from social and behavioral research is needed for pursuing this strategy of knowledge generation about micro-processes. Accordingly, our proposal points to a unique role that social scientists could play in Big Data research.