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The dynamics of individuals is of essential importance for understanding theevolution of social systems. Most existing models assume that individuals indiverse systems, ranging from social networks to e-commerce, all tend to whatis already popular. We develop an analytical time-aware framework which showsthat when individuals make choices -- which item to buy, for example -- inonline social systems, a small fraction of them is consistently successful indiscovering popular items long before they actually become popular. We arguethat these users, whom we refer to as discoverers, are fundamentally differentfrom the previously known opinion leaders, influentials, and innovators. We usethe proposed framework to demonstrate that discoverers are present in a widerange of systems. Once identified, they can be used to predict the futuresuccess of items. We propose a network model which reproduces the discoverypatterns observed in the real data. Furthermore, data produced by the modelpose a fundamental challenge to classical ranking algorithms which neglect thetime of link creation and thus fail to discriminate between discoverers andordinary users in the data. Our results open the door to qualitative andquantitative study of fine temporal patterns in social systems and havefar-reaching implications for network modeling and algorithm design.

Identification and modeling of discoverers in online social systems