Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 023136 |
| Journal | Chaos |
| Volume | 29 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2019 |
Fingerprint
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Mathematical Physics
- Physics and Astronomy(all)
- Applied Mathematics
Cite this
A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics. / Wang, Le Zhi; Zhao, Zhi Dan; Jiang, Junjie; Guo, Bing Hui; Wang, Xiao; Huang, Zi Gang; Lai, Ying-Cheng.
In: Chaos, Vol. 29, No. 2, 023136, 01.02.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A model for meme popularity growth in social networking systems based on biological principle and human interest dynamics
AU - Wang, Le Zhi
AU - Zhao, Zhi Dan
AU - Jiang, Junjie
AU - Guo, Bing Hui
AU - Wang, Xiao
AU - Huang, Zi Gang
AU - Lai, Ying-Cheng
PY - 2019/2/1
Y1 - 2019/2/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85062440081&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062440081&partnerID=8YFLogxK
U2 - 10.1063/1.5085009
DO - 10.1063/1.5085009
M3 - Article
C2 - 30823725
AN - SCOPUS:85062440081
VL - 29
JO - Chaos (Woodbury, N.Y.)
JF - Chaos (Woodbury, N.Y.)
SN - 1054-1500
IS - 2
M1 - 023136
ER -