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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 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.

The purpose of this paper is to study how web site quality affects traffic performance.

An experimental design is employed to study how web quality affects traffic performance. A revamping of the experimental web site was used as the treatment, targeting visitors' perceived quality of the web site. Four traffic performance measures, page: views, visitor count, daily registrations, and average duration are tracked, and t‐tests are performed on pre‐treatment and post‐treatment data.

The analysis shows very positive responses among members; visitor count, page views and average duration increased for opt‐in and opt‐out members. For visitor count, even non‐members showed increases. However, daily registration, which measures how many non‐members become members each day, did not change. Non‐members visited more, but neither viewed more pages, nor stayed longer. Average duration is identified as the key factor for discerning visitor groups.

The experimental web site belongs to one web site category. The generalization is subject to reasoning by practitioners.

It was found that: to increase membership, alternative schemes must be employed, perhaps along the lines of a non‐technical approach; to acquire more members, do not focus on converting known non‐members. Those with the same demographic profile as existing members should be targeted; and the question must be asked whether the fact that opt‐in members are stickier than opt‐out members is a trait or a consequence of opt‐in members receiving e‐mails periodically, while opt‐out members chose not to receive e‐mails.

With few existing traffic experiments in the literature, this study is unique, as are its implications.

This paper proposes a revised analytical model for accounting professionals that can be used to evaluate the financial well being of innovative companies that rely on earnings management practices (EM) for their growth. Through an analysis of corporate governance, financial reporting standards, and ratio analysis this paper reaches the conclusion that Enron extended previously researched earnings management practices that could have been detected in early 2000. Results of the analysis indicate that by using price book, price earnings multiple, net margin percentage, and return on assets, and taking into consideration the so‐called risk management activities which seemed to disguise highly volatile speculative derivative‐based activities, Enron was headed for implosion at least one year before its collapse.

Gender and geographical imbalance in production and impact levels is a pressing issue in global knowledge production. Within Health Sciences, while some studies found stark gender and geographical biases and inequalities, others found little empirical evidence of this marginalization. The purpose of the study is to clear the ambiguity concerning the topic.

Based on a comprehensive and systematic analysis of Health Sciences research data downloaded from the Scival (Scopus/Scimago) database from 2017 to 2020 (n = 7,990), this study first compares gender representation in research productivity, as well as differences in terms of citation per document, citations per document view and view per document scores according to geographical location. Additionally, the study clarifies whether there is a geographic bias in productivity and impact measures (i.e. citation per document, citations per document view and view per document) moderated by gender.

Results indicate that gender inequalities in productivity are systematic at the overall disciplinary, as well as the subfield levels. Findings also suggest statistically significant geographical differences in citation per document, citations per document view, and view per document scores, and interaction effect of gender over the relation between geography and (1) the number of citations per view and (2) the number of views per document.

This study contributes to scientometric studies in health sciences by providing insightful findings about the geographical and gender bias in productivity and impact across world regions.

This paper aims to describe the use of web statistics by libraries, archives and museums in The Netherlands.

Three methods were applied: a survey among more than 100 institutions, interviews and content analysis of annual reports.

Most institutions gather web statistics. A large variety of packages is used, which hinders comparison among institutions. Web statistics are used for practical purposes, such as adapting the web site or setting priorities for further digitization, and as a critical success factor. Most archives and museums mention web statistics in their annual report. Usually, they do not explain the data and do not provide background information, which makes it difficult to interpret them.

The sample represented institutions with above average interest in, or experience with, digitizing.

This inventory may stimulate large‐scale use of web statistics in cultural heritage institutions and be the first step towards standardization.

This study is the first attempt to investigate the use of web statistics in cultural heritage institutions in The Netherlands.

The purpose of this study is to explore current practices, challenges and technological needs of different data repositories.

An online survey was designed for data repository managers, and contact information from the re3data, a data repository registry, was collected to disseminate the survey.

In total, 189 responses were received, including 47% discipline specific and 34% institutional data repositories. A total of 71% of the repositories reporting their software used bespoke technical frameworks, with DSpace, EPrint and Dataverse being commonly used by institutional repositories. Of repository managers, 32% reported tracking secondary data reuse while 50% would like to. Among data reuse metrics, citation counts were considered extremely important by the majority, followed by links to the data from other websites and download counts. Despite their perceived usefulness, repository managers struggle to track dataset citations. Most repository managers support dataset and metadata quality checks via librarians, subject specialists or information professionals. A lack of engagement from users and a lack of human resources are the top two challenges, and outreach is the most common motivator mentioned by repositories across all groups. Ensuring findable, accessible, interoperable and reusable (FAIR) data (49%), providing user support for research (36%) and developing best practices (29%) are the top three priorities for repository managers. The main recommendations for future repository systems are as follows: integration and interoperability between data and systems (30%), better research data management (RDM) tools (19%), tools that allow computation without downloading datasets (16%) and automated systems (16%).

This study identifies the current challenges and needs for improving data repository functionalities and user experiences.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0204

The purpose of the study is to evaluate certain characteristics of the videos of the software Koha and DSpace posted on YouTube. Since YouTube has the potential to provide the content creator to share their knowledge and experience through their content which has become much more beneficial to the information seeker. Nowadays, people search for queries or tutorial videos on YouTube very often to earn a better understanding of the term. Sentiment analysis of the viewers' opinion of the videos is another purpose of this study.

Dataset for evaluating the characteristic of the videos of Koha and DSpace was extracted by using Webometric Analyst by creating YouTube API. Once retrieval of data was completed, a manual verification was enhanced to filter out spam videos unrelated to the scope. After the confirmation of authentic relatable videos, seeking the video's id as query, the comments per video were extracted using Webometric Analyst. For opinion mining, the Parallel Dots API web service was used in Google Sheets as an addon function. The sentiment, multilingual sentiment, emotion, intention and word frequency of the viewers' opinion was examined with the help of certain default functionalities.

Webometric Analyst extracted a total of 461 and 397 videos of Koha and DSpace, respectively, uploaded on the YouTube platform. The findings of the study indicate that the growth rate of videos on Koha is decreasing, while the number of videos uploaded on DSpace is gradually increased in the last 10 years. The highest number of videos posted in 1–20 min duration category with mostly high definition (HD) with standard YouTube license and prominently in the English language. The sentiment analysis of the total extracted comments on Koha and DSpace videos found to be 2043 and 862 comments, respectively, among whom “Positive” comments are mostly found and with “Happy” emotion can be highly detected with most supportive “Feedback” intention on both Koha and DSpace videos. The top word frequency signifies that the users of both the software are using the comments section of the videos on YouTube to ask and provide troubleshooting help to each other.

The present study has some limitations too; the dataset for the study includes only those videos whose title, description or keywords sections had the query terms “Koha” or “DSpace” there are chances that some videos would have been left out from the dataset related to these software.

This is the first paper to evaluate the characteristics and sentiment of both the videos Koha and DSpace. Through this, the popularity, likeness and dislike and the impact of the contents of the videos uploaded will be disclosed, and creators can make an improvement by referring this, and the seekers will adapt to the use of correct and authentic information.

Vaccine hesitancy is a long-standing issue among both the general population and health communicators. This study aims to ascertain the inclination and the reasons for vaccine hesitancy by conducting content analysis and sentiment analysis of the perspectives expressed in comments on videos related to vaccine hesitancy uploaded from India on YouTube.

The assessment of the sentiments of the vaccine-hesitant population is done using Valence Aware Dictionary and sEntiment Reasoner sentiment analysis module implemented with Python’s NLTK library to automatically determine the sentiments of the comments. Manual content analysis was performed on 60.09% viewer comments randomly selected from the total comments in 238 videos on vaccine hesitancy originated from India and labelled each comment with labels “Anti”, “Pro”, “Confused”, “Not Applicable” and “Unrelated” labels.

The study found “Mistrust-Government policies”, “Fear-health related consequences”, “Mistrust-Scientific research”, “Vaccine effectiveness and efficacy” and “Misinformation/myths” as the top five determinants for vaccine hesitancy, whereas “Religious beliefs”, “Fear-Economic consequences”, “Side Effects- short-term” and “Fear-mode of administration” found to be the lesser cited reasons for vaccine hesitancy. However, the study also investigates changes in the inclination of Indian commenters towards vaccine hesitancy and revolving issues over time.

Public health policymakers and health communicators may find the study useful in determining vaccine hesitancy factors in India.

The originality of this study lies in its approach. To date, no sentiment analysis has been conducted on the content released on YouTube by Indian content creators regarding pro- and anti-vaccination videos. This inquiry seeks to fill this research gap.