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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.


Related in: MedlinePlus

One single-period diffusion process through τk to τk+1.At update time τk, the impulsive intensity of  indicates the newly recruited audience, while removed population  is the audience stopped watching, which is only counted at every update time. During non-update periods, external influence αS(t) and interpersonal spreading λS(t)I(t) occur in every time unit. A concave feverish function is used to fit the imbalance of the on-demand streaming distribution.
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pone.0163432.g002: One single-period diffusion process through τk to τk+1.At update time τk, the impulsive intensity of indicates the newly recruited audience, while removed population is the audience stopped watching, which is only counted at every update time. During non-update periods, external influence αS(t) and interpersonal spreading λS(t)I(t) occur in every time unit. A concave feverish function is used to fit the imbalance of the on-demand streaming distribution.

Mentions: Therefore, we propose a PI-SIR model for online TV series diffusion. Fig 2 illustrates one single-period diffusion process from τk to τk+1.


Modeling Periodic Impulsive Effects on Online TV Series Diffusion
One single-period diffusion process through τk to τk+1.At update time τk, the impulsive intensity of  indicates the newly recruited audience, while removed population  is the audience stopped watching, which is only counted at every update time. During non-update periods, external influence αS(t) and interpersonal spreading λS(t)I(t) occur in every time unit. A concave feverish function is used to fit the imbalance of the on-demand streaming distribution.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0163432.g002: One single-period diffusion process through τk to τk+1.At update time τk, the impulsive intensity of indicates the newly recruited audience, while removed population is the audience stopped watching, which is only counted at every update time. During non-update periods, external influence αS(t) and interpersonal spreading λS(t)I(t) occur in every time unit. A concave feverish function is used to fit the imbalance of the on-demand streaming distribution.
Mentions: Therefore, we propose a PI-SIR model for online TV series diffusion. Fig 2 illustrates one single-period diffusion process from τk to τk+1.

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.


Related in: MedlinePlus