We analyze five big data sets from a variety of online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors. For example, there is linear growth associated with online recommendation and sharing platforms, a plateaued (or an "S"-shape) type of growth behavior in a web service devoted to helping users to collect bookmarks, and an exponential increase on the largest and most popular microblogging website in China. Does a universal mechanism with a common set of dynamical rules exist, which can explain these empirically observed, distinct growth behaviors? We provide an affirmative answer in this paper. In particular, inspired by biomimicry to take advantage of cell population growth dynamics in microbial ecology, we construct a base growth model for meme popularity in OSNs. We then take into account human factors by incorporating a general model of human interest dynamics into the base model. The final hybrid model contains a small number of free parameters that can be estimated purely from data. We demonstrate that our model is universal in the sense that, with a few parameters estimated from data, it can successfully predict the distinct meme growth dynamics. Our study represents a successful effort to exploit principles in biology to understand online social behaviors by incorporating the traditional microbial growth model into meme popularity. Our model can be used to gain insights into critical issues such as classification, robustness, optimization, and control of OSN systems.With advances in information technologies, a novel class of complex dynamical systems has emerged: online social networking (OSN) systems. The complexity of OSN systems is enormous: posting and sharing of messages by users, sudden occurrence of breaking news events, and random drifts in user interests, etc., all leading to drastic variations of the network structure and dynamics with time and making (big) data analysis an essential approach to uncovering the inner dynamical working of these systems. A phenomenon that has attracted recent attention is growth dynamics of memes such as news, ideas, knowledge or rumors in OSN systems. Previous models focusing on the individual level were unable to account for the common phenomenon of group popularity, raising the need to develop a comprehensive model that incorporates heterogeneity of users and memes to describe quantia) Electronic tatively the collective dynamics of meme popularity. Another challenge in the construction of a model for meme popularity lies in its distinct growth behaviors in different OSN systems. Our analysis of five big data sets from diverse social networking platforms has revealed at least three characteristically different types of behaviors: linear, plateaued (or "S" shaped), and exponential growth in time. Is it possible to construct a single model that can explain the distinct growth behaviors? This paper provides an affirmative answer. The general principle underlying our work is that, wh...
The development of online social environments has changed the manner of social interaction and communication, which are driven by individual human actions. Thus temporal variations in interaction networks are deeply impacted by the temporal dimension of human activity. In this paper, we address this issue through a detailed analysis on the retweets and comments of 550,000 Twitter users. We propose a temporal network model to represent the interaction network on Twitter, in which each node contains an activity window and the emergence of the edges between nodes are dependent on it. Specifically, the activity window is defined as the backtracking length from the message flow posted by the user’s friend, which represents the user’s social ability. It complies with a power-law distribution with an exponential cut-off. The interaction network is sparser and more clustered than the followee-follower network, in which the interaction stability and burstiness fluctuate with the activity window or with the degree to which the two users are involved in the communication. Finally, the effect of activity window on the aggregating degrees of the interaction network is examined.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.