Search results

YouTube's vast and engaged user base makes it central to firms' digital marketing effort. With extant studies focusing on viewers' post-view engagement behavior, however, research into what motivates viewers to click on and watch YouTube videos is scarce. This study investigates the implications of marketers' video optimization practices for video views on YouTube.

The study employed a data set of videos (N = 4,398) gathered by scraping YouTube's trending list. Using a combination of text and sentiment analysis, the study measured four video optimization practices: information content of video titles, emotional intensity of video titles, information content of video descriptions and volume of video tags. It then analyzed the effect of these video optimization practices on video views.

The study finds that greater availability of information in video titles is negatively associated with video views, whereas intensity of negative emotional sentiment in video titles is positively associated with video views. Further, greater availability of information in video descriptions is positively associated with video views. Finally, an inverted U-shaped relationship is found between volume of video tags and video views. Up to 17 video tags can contribute to more video views; however, beyond 17 tags, the relationship turns negative.

This study investigates the effect of marketers' video optimization practices on video views. While extant studies mainly focus on viewers' post-view engagement behavior, such as liking, commenting on and sharing videos, this study examines video views. Similarly, extant studies investigate videos' internal content, while this study investigates elements of the video metadata.

This study aims to analyze whether average video watch time or click-through rates (CTR) on YouTube videos are more closely associated with high numbers of views per subscriber using linear regressions.

In 2018, YouTube began releasing CTR data to its video creators. Since 2012, YouTube has emphasized how it favors watch time over clicks in its recommendations to viewers. To the best of the author’s knowledge, this is the first academic study looking at that CTR data to test what matters more for views on YouTube. Is watch time or CTR more important to getting views on YouTube?

The author analyzed new video releases on YouTube. This paper finds almost no or limited evidence that higher percent audience retention or total average watch time per view, respectively, are associated with more views on YouTube. Instead, videos with higher CTR got significantly more views.

The author knows no other study that tests the relative importance of CTR or watch time per view in predicting views for new videos on YouTube.

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

The purpose of this paper is to seek reasons for some videos going viral over YouTube (a type of social media platform).

Using YouTube APIs (Application Programming Interface) and Webometrics analyst tool, the authors collected data on about 100 all-time-most-viewed YouTube videos and information about the users associated with the videos. The authors constructed and tested an empirical model to understand the relationship among users’ social and non-social capital (e.g. User Age, Gender, View Count, Subscriber, Join Date, Total Videos Posted), video characteristics (Post Date, Duration, and Video Category), external network capital (in-links and hit counts), and Virality (Likes, Dislikes, Favorite Count, View Count, and Comment Count). Partial least square and Webometric analysis was used to explore the association among the constructs.

Among other findings, the results showed that popularity of the videos was not only the function of YouTube system per se, but that network dynamics (e.g. in-links and hits counts) and offline social capital (e.g. fan base and fame) play crucial roles in the viral phenomenon, particularly view count.

The authors for the first time constructed and tested an empirical model to find out the determinants of viral phenomenon over YouTube.

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.

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.

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.

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.

The body of scholarship on YouTube is an expanding area of scholarly inquiry. Existent research indicates that music videos are one of the most salient features of YouTube. Interactionist research about popular music has provided important insights through interviews with fans and audience members; however, this work has yet to examine audience engagement with music videos on YouTube. Using Qualitative Media Analysis, I illustrate how the researcher of popular music can work with user comments collected from YouTube. Thematic understandings largely consistent with nostalgia that emerged from an analysis of user-generated comments in response to selected music videos on YouTube are explored. I conclude by suggesting some directions for future research.

This paper aims to examine the recommendation system of the video-sharing website YouTube to study how control of users is effected on online platforms.

This paper conceptualises algorithmic systems as protocols – technological and social infrastructures that both facilitate and govern interactions between autonomous actors (Galloway and Thacker, 2004, 2007). It adopts a netnographic approach (Kozinets, 2002) to study not only the formal, technological systems of the platform but also the systems as they were made sense of, understood and enacted upon by actors. It relies both on information as revealed by the organisation itself, as well as discussions between lay users in online forums and press coverage.

The results of this study indicate that the ways in which platforms selectively facilitate interactions between users constitute a form of control. While maintaining the appearance of an open and neutral marketplace, interactions on the platform are in fact highly structured. The system relies on the surveillance of user interactions to rapidly identify and propagate marketable contents, so as to maximise user “engagement” and ad revenue. The systems place few demands or restrictions on individual users, instead control is effected in a probabilistic fashion, over the population of users as a whole, so as to, in aggregate, accomplish organisational goal.

This paper contributes to the literature on accounting and control practices in online spaces, by extending the notion of control beyond overt rankings and evaluations, to the underlying technical and social infrastructures that facilitate and shape interactions.

The purpose of this paper is to present brief YouTube life stories to learn about how extensive users experience YouTube use and manage (or fail to manage) their use. It also explores the consequences of different types of extensive use.

In this paper, a biographical approach was used. Nine students who used YouTube for two or more hours every day were guided to tell life stories of their introduction to YouTube, subsequent use and critical events associated with YouTube use. Thematic analysis distinguished between non-problematic, compulsive and addicted users. Three single case life stories illustrate the experiences of users in each category.

These extensive YouTube users tell similar stories of informal learning from early interaction with the platform. For some, extensive YouTube use became problematic; for others, it remained functional. Similar to other social platforms, users unable to regulate use became compulsive users and some users can become addicted. While the symptoms of YouTube addiction are similar to other online addictions, compulsive YouTube use is driven more by algorithm-generated content chaining than overt social interaction.

The paper introduces life stories as a way to present case studies of social media use. The distinction between extensive, but functional, and problematic YouTube use illustrates how extensive social media use is not necessarily dysfunctional. User education for self-regulation of YouTube use is recommended.

This study aims to investigate replies to the top 10 comments under Always “Like a Girl” YouTube femvertising video to gauge consumers’ responses regarding femvertising as well as relationships among commenters.

This study adopted a mixed research methods design. A user analysis and a qualitative content analysis were conducted to examine the replies of the top 10 comments with the most replies to reveal not only the topics but also relationships and patterns among those comments and commenters.

The user analysis found that across all the 10 comment-and-reply units, in 8 units, the user of the original primary comment, the conversation starter, was also the user who was targeted most often. The qualitative content analysis revealed four themes from the 10 comment-and-reply units: multilayered emotional responses, a gendered society, complex coexisting relationships and a melting pot.

The findings of this research offer significant extensions to the understanding of public sphere theory within the contemporary digital media landscape. By analyzing the nature of replies to digital advertisements, the study illuminates how various types of user engagement–whether it be inquiry, laudation, debate, or flame–play a critical role in shaping the digital public sphere.

The study underscores the importance for marketers to scrutinize both comments and replies to effectively utilize femvertising on social media, particularly YouTube. By understanding the emotional dynamics of user interactions, marketers can craft strategies that evoke positive responses and mitigate negative ones. Engaging with users who are open to changing their views or mediating discussions can also be beneficial, as can the use of AI tools to maintain focus on the content rather than on individual commenters. Such approaches can enhance the perception of femvertising campaigns and foster a more constructive dialogue within the social media space.

This study contributes to the literature by investigating the replies of comments, interactions, relationships and patterns among YouTube commenters that may generate valuable insights for advertisers and marketers to be aware of the possible issues and monitor the sentiment of commentaries, thus, developing effective strategies to better connect with consumers. This extends the understanding of public sphere theory in the contemporary digital media landscape.

This study investigates two important research questions. First, does YouTube advertising create value for customers to activate their inspired-by state (motivation), or does customer engagement in advertised brands have a mediating role? Second, does the inspired-by state influence customers’ inspired-to state (action) to purchase the advertised brand?

This study employs Ducoffe’s advertising value model to investigate how customers’ engagement mediates perceived advertising value and their inspired-by state. The authors split customer inspiration into two primary states: inspired-by (i.e. the early interest in taking action) and inspired-to (i.e. the intention to act), demonstrating that the latter is positively influenced by the former. The study employs SmartPLS V3.2.9 to analyze survey data from 360 respondents in Pakistan – an emerging market.

This study found that informativeness, entertainment, creativity and incentives exerted a significant positive impact on perceived advertising value. The perceived advertising value of YouTube ads fails to influence customers’ inspired-by state directly; however, customer engagement positively mediates the relationship between the perceived advertising value of YouTube and customers’ inspired-by state. Finally, the customers’ inspired-by state is successfully converted into an inspired-to state.

This study has numerous practical implications for advertisers and marketers seeking to optimize social media advertising and marketing performance.

YouTube ads shape consumer behavior, empowering informed choices; authentic engagement transforms the advertising landscape.

This study is the first to examine the perceived advertising value of YouTube ads for eliciting customers’ inspired-by state, assessing the mediating role of customer engagement as a mechanism. Moreover, the authors examine the role of customers’ inspired-by state as a predictor of the inspired-to state.