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Modeling Periodic Impulsive Effects on Online TV Series Diffusion

View Article: PubMed Central - PubMed

ABSTRACT

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.

Conclusion: 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.

No MeSH data available.


Two-step fitting results for the Farmer dataset.
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pone.0163432.g008: Two-step fitting results for the Farmer dataset.

Mentions: As listed in Table 3, the six are almost larger than 0.9, and the Vss are very close to 0 (except Farmer’s), which implies that the ODSA of every period might exhibit the same characteristics from a perspective of collective behavior. Therefore, we can only use one set of estimated parameters of to fit the feverish function in the second step as follows:f^(i)=1U·∑k=1UV(τk+i)V(τk,τ),∑i=1τf^(i)=1;∑i=1τV′(τk+i)=V′(τk,τ);i=1,...,τ.(20)where V(τk + i) and V(τk, τ) are the actual values and V′(τk + i) and V′(τk, τ) are the fitting values. The results for are listed in Table 2. Figs 3–8 show the two-step fitting results for the six datasets.


Modeling Periodic Impulsive Effects on Online TV Series Diffusion
Two-step fitting results for the Farmer dataset.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036804&req=5

pone.0163432.g008: Two-step fitting results for the Farmer dataset.
Mentions: As listed in Table 3, the six are almost larger than 0.9, and the Vss are very close to 0 (except Farmer’s), which implies that the ODSA of every period might exhibit the same characteristics from a perspective of collective behavior. Therefore, we can only use one set of estimated parameters of to fit the feverish function in the second step as follows:f^(i)=1U·∑k=1UV(τk+i)V(τk,τ),∑i=1τf^(i)=1;∑i=1τV′(τk+i)=V′(τk,τ);i=1,...,τ.(20)where V(τk + i) and V(τk, τ) are the actual values and V′(τk + i) and V′(τk, τ) are the fitting values. The results for are listed in Table 2. Figs 3–8 show the two-step fitting results for the six datasets.

View Article: PubMed Central - PubMed

ABSTRACT

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.

Conclusion: 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.

No MeSH data available.