Abstract
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.
Highlights
Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond
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
We introduce a simple network model which reproduces the discovery patterns observed in the real data
Summary
Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. 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 our framework to demonstrate the presence of discoverers in data from a number of real systems and discuss the relation between discoverers and other related concepts such as opinion leaders[17,18,19,20,21], and innovators[22,23,24] Besides none of these concepts providing a full explanation for the behavior of discoverers, the main strength of our contribution lies in a well-defined quantitative method to identify the users that do not follow the omnipresent preferential attachment rule. We grow model networks and show that they exhibit similar discovery patterns as those observed in the real data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
