Identification and impact of discoverers in online social systems
- Medo, Matúš Department of Physics, University of Fribourg, Switzerland
- Mariani, Manuel Sebastian Department of Physics, University of Fribourg, Switzerland
- Zeng, An Department of Physics, University of Fribourg, Switzerland - School of Systems, Science, Beijing Normal University, Beijing, China
- Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland
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30.09.2016
Published in:
- Scientific Reports. - 2016, vol. 6, p. 34218
English
Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 278706
- DOI 10.1038/srep34218
- Persistent URL
- https://folia.unifr.ch/unifr/documents/305321
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