"Big Data" : big gaps of knowledge in the field of internet science

TLDR

Research on Big Data has gained momentum, especially in online network analysis, where macro‑characteristics such as small‑world properties are known but the underlying micro‑processes remain poorly understood, and current models are often chosen for mathematical convenience. The authors propose a strategy to uncover micro‑processes that align with actual online behavior, enabling the selection of mathematically tractable models for online network formation and evolution. This strategy relies on integrating insights from social and behavioral research to generate knowledge about micro‑processes. The proposal highlights a unique role for social scientists in Big Data research.

Abstract

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.