Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
BACKGROUND
Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data.


METHODS
We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution.


RESULTS
We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation.


CONCLUSIONS
To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities also represents a highly correlated analysis tool to evaluate the advertising value of TV series.
College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.
Peihua
http://orcid.org/0000-0001-5150-6464
P
Fu
College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.
Anding
http://orcid.org/0000-0001-8162-6213
A
Zhu
College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.
Qiwen
Q
Fang
College of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang, P.R. China.
Xi
X
Wang
ScientificWorldJournal. 2014 Mar 23;2014:914907
24795541
Nonlinear Dyn. 2010;59(3):503-513
32214666
Phys Rev E Stat Nonlin Soft Matter Phys. 2014 Oct;90(4):042803
25375545
PLoS One. 2011;6(7):e20648
21857891
Phys Rev Lett. 2009 Jul 17;103(3):038702
19659326
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Jul;92(1):012820
26274238
Science. 2009 Feb 6;323(5915):721-3
19197046