Purpose

YouTube allows its users to upload and view videos on its platform. YouTube provides notification to the subscribers whenever a channel uploads a new video thereby making the channel subscribers the potential viewers of the video. And thus, they are the first to come to know about any new offering. But later on, the view count also increases due to virality, that is, mass sharing of the content by the users on different social media platforms similar to word-of-mouth in the field of marketing. Therefore, the purpose of this paper is to examine different diffusion patterns as they can help to inflate traffic and generate revenue.

Design/methodology/approach

YouTube's view count grows majorly through virality. The pattern of view count growth has generally been considered unimodal in most of the available research in the field of YouTube. In the present work, the growth process due to views through the subscribers and views due to word-of-mouth (virality) is presented. Considering that the impact of virality in view count growth comes later in the video life cycle; the viewing patterns of both the segments have been mathematically modeled; independently.

Findings

Different models have been proposed to capture the view count growth pattern and how the impact of virality changes the view count growth curve and thereby results in a multimodal curve structure. The proposed models have been verified on various view count data sets of YouTube videos using SPSS (Statistical Package for the Social Sciences), and their ranks have been determined using a weighted criteria–based approach. The results obtained clearly depict the presence of many modes in the life cycle of view counts.

Originality/value

Till now, the literature is evident of the video life cycle following a bell shape curve. This study claims that the initial thrust is by subscribers and then the contribution in the view count by people watching via word-of-mouth comes into picture and brings in another hump in the growth curve.

中文翻译：

---

为 YouTube 视频建模观看次数动态：多模式视角

目的

YouTube 允许其用户在其平台上上传和查看视频。每当频道上传新视频时，YouTube 都会向订阅者提供通知，从而使频道订阅者成为视频的潜在观众。因此，他们是第一个知道任何新产品的人。但后来，由于病毒式传播，即用户在不同社交媒体平台上大量分享内容，类似于营销领域的口碑传播，观看次数也会增加。因此，本文的目的是研究不同的扩散模式，因为它们有助于增加流量并产生收入。

设计/方法/方法

YouTube 的观看次数主要通过病毒式传播而增长。在 YouTube 领域的大多数可用研究中，观看次数增长的模式通常被认为是单峰的。在目前的工作中，呈现了由于订阅者的观看次数和口碑（病毒式传播）带来的观看次数的增长过程。考虑到病毒式传播对观看次数增长的影响出现在视频生命周期的后期；两个片段的观看模式都经过数学建模；独立。

发现

已经提出了不同的模型来捕获观看次数增长模式以及病毒式传播的影响如何改变观看次数增长曲线，从而导致多模态曲线结构。所提出的模型已经使用 SPSS（社会科学统计包）在 YouTube 视频的各种观看次数数据集上进行了验证，并且它们的排名是使用基于加权标准的方法确定的。获得的结果清楚地描述了在视图计数的生命周期中存在多种模式。

原创性/价值

到目前为止，文献表明视频生命周期遵循钟形曲线。这项研究声称最初的推动力来自订阅者，然后人们通过口耳相传的方式对观看次数的贡献逐渐显现出来，并在增长曲线上带来了另一个驼峰。