The global number of Youtube users in was forecast to continuously increase between 2024 and 2029 by in total 232.5 million users (+24.91 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 1.2 billion users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Africa and South America.

As of June 2022, more than *** hours of video were uploaded to YouTube every minute. This equates to approximately ****** hours of newly uploaded content per hour. The amount of content on YouTube has increased dramatically as consumer’s appetites for online video has grown. In fact, the number of video content hours uploaded every 60 seconds grew by around ** percent between 2014 and 2020. YouTube global users Online video is one of the most popular digital activities worldwide, with ** percent of internet users worldwide watching more than ** hours of online videos on a weekly basis in 2023. It was estimated that in 2023 YouTube would reach approximately *** million users worldwide. In 2022, the video platform was one of the leading media and entertainment brands worldwide, with a value of more than ** billion U.S. dollars. YouTube video content consumption The most viewed YouTube channels of all time have racked up billions of viewers, millions of subscribers and cover a wide variety of topics ranging from music to cosmetics. The YouTube channel owner with the most video views is Indian music label T-Series, which counted ****** billion lifetime views. Other popular YouTubers are gaming personalities such as PewDiePie, DanTDM and Markiplier.

Introduction

YouTube vs Vimeo Statistics: In the world of online video platforms, YouTube and Vimeo hold distinct yet influential roles. As a Google property, YouTube has grown to become the world's second-largest search engine, with billions of hours watched daily. Vimeo is oriented toward professionals and businesses seeking hosting services with supported high-quality video and no ads, with advanced collaboration and enterprise-level features.

This article will present some YouTube vs Vimeo statistics for 2025, in comparison with users, engagement, revenue, and market positioning.

The number of Youtube users in India was forecast to continuously increase between 2024 and 2029 by in total 222.2 million users (+34.88 percent). After the ninth consecutive increasing year, the Youtube user base is estimated to reach 859.26 million users and therefore a new peak in 2029. Notably, the number of Youtube users of was continuously increasing over the past years.User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Sri Lanka and Nepal.

Introduction

Vimeo Statistics: With the help of its motto "Vimeo for the Serious," video sharing platform Vimeo emerged in 2004 and has steadily been focusing on high-quality content and serving the creators who desire to offer their work professionally.

Unlike YouTube, aimed at the more recreational consumption of content, Vimeo has been and continues to attract interest mostly from businesses, filmmakers, artists, and other members of different professions. As it stands today in 2025, Vimeo continues to thrive with its variety of offerings for its atypical user base, including subscription services, business solutions, and tools for creators to connect with audiences.

This article focuses on the most recent Vimeo statistics and discusses the platform's financials, users, geography, and trends.

Please cite the following paper when using this dataset:

N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: https://doi.org/10.48550/arXiv.2406.07693)

Abstract

This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

The YouTube-100M data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”). Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced.

The dataset consists of three files: the metadata, comments, and captions of the ground-truth dataset videos collected and manually reviewed in this paper.

Video Metadata: "groundtruth_videos.json": Contains the metadata of our manually reviewed ground-truth dataset videos. The ground-truth dataset includes 1,197 science, 1,325 pseudoscience, and 3,212 irrelevant videos. More specifically, it includes the metadata of videos related to the following pseudoscientific topics: COVID-19: (607 science, 368 pseudoscience, 721 irrelevant videos) Anti-vaccination (363 science, 394 pseudoscience, and 1,060 irrelevant videos) Anti-mask (65 science, 188 pseudoscience, and 724 irrelevant videos) Flat Earth (162 science, 375 pseudoscience, and 707 irrelevant videos)

Note, that 600 of the videos in this dataset include the "annotation.manual_review_label" attribute, which is the label assigned by the first author of this paper to evaluate the performance of the crowdsourced annotation process.

Video Metadata Description: "search_term": The search terms used to search YouTube and retrieve these videos during our data collection. It can be one of the following search terms: 'covid-19', 'coronavirus', 'anti-vaccination', 'anti-vaxx', 'anti-mask', or 'flat earth'. "annotation.annotations": The list of the three annotations assigned to each video by our crowdsourced annotators. "annotation.label": The annotation label assigned to the video based on the majority agreement of the crowdsourced annotators. "annotation.manual_review_label": The label assigned by the first author of this paper to evaluate the performance of the crowdsourced annotation process. "isSeed": 0 if the video is a seed video of our data collection, 1 if it is a recommended video of a seed video.

"relatedVideos": The recommended videos of the given video as returned by the YouTube Data API.

Video Comments:

"groundtruth_videos_comments_ids.json": Includes the identifiers of the comments of our ground-truth videos.

Video Transcripts:

"groundtruth_videos_transcripts.json": Includes the captions of our ground-truth videos. If you use this dataset in any publication, of any form and kind, please cite using this data.

This statistic presents the most popular video content categories on YouTube worldwide, ranked by market share. As of December 2018, people and blogs were the most popular YouTube content category based on share of available videos. The category accounted for 32 percent of public videos on the platform.

Social behavior has a fundamental impact on the dynamics of infectious diseases (such as COVID-19), challenging public health mitigation strategies and possibly the political consensus. The widespread use of the traditional and social media on the Internet provides us with an invaluable source of information on societal dynamics during pandemics. With this dataset, we aim to understand mechanisms of COVID-19 epidemic-related social behavior in Poland deploying methods of computational social science and digital epidemiology. We have collected and analyzed COVID-19 perception on the Polish language Internet during 15.01-31.07(06.08) and labeled data quantitatively (Twitter, Youtube, Articles) and qualitatively (Facebook, Articles and Comments of Article) in the Internet by infomediological approach.

• manually labelled1,449 articles / Facebook posts from Lower Silesia (facebook_articles_lower_silesia.zip) and 111 texts from outside this region;

-manually labelled 1000 most popular tweets (twits_annotated.xlsx) with cathegories is_fake (categorical and numeric) topic and sentiment;

-extracted 57,306 representative articles (articles_till_06_08.zip) in Polish using Eventregitry.org tool in language Polish and topic "Coronavirus" in article body;

• extracted 1,015,199 (tweets_till_31_07_users.zip and tweets_till_31_07_text.zip) and Tweets from #Koronawirus in language Polish using Twitter API.

• collected 1,574 videos (youtube_comments_till_31_07.zip and youtube_movie.csv) with keyword: Koronawirus on YouTube and 247,575 comments on them using Google API;

• We supplemented the media observations with an analysis of 244 social empirical studies till 25.05 on COVID-19 in Poland (empirical_social_studies.csv).

Reports and analyzes and coding books can be found in Polish at: http://www.infodemia-koronawirusa.pl

Main report (in Polish) https://depot.ceon.pl/handle/123456789/19215

This repository contains data and replication code for the article "Social Media Sellout - The Increasing Role of Product Promotion on YouTube", published in Social Media and Society. https://doi.org/10.1177/2056305118786720

Associative Tag Recommendation Exploiting Multiple Textual FeaturesFabiano Belem, Eder Martins, Jussara M. Almeida Marcos Goncalves In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, July. 2011AbstractThis work addresses the task of recommending relevant tags to a target object by jointly exploiting three dimen- sions of the problem: (i) term co-occurrence with tags preassigned to the target object, (ii) terms extracted from mul- tiple textual features, and (iii) several metrics of tag relevance. In particular, we propose several new heuristic meth- ods, which extend previous, highly effective and efficient, state-of-the-art strategies by including new metrics that try to capture how accurately a candidate term describes the object’s content. We also exploit two learning to rank techniques, namely RankSVM and Genetic Programming, for the task of generating ranking functions that combine multiple metrics to accurately estimate the relevance of a tag to a given object. We evaluate all proposed methods in various scenarios for three popular Web 2.0 applications, namely, LastFM, YouTube and YahooVideo. We found that our new heuristics greatly outperform the methods on which they are based, producing gains in precision of up to 181%, as well as another state-of-the-art technique, with improvements in precision of up to 40% over the best baseline in any scenario. Some further improvements can also be achieved, in some scenarios, with the new learning-to-rank based strategies, which have the additional advantage of being quite flexible and easily extensible to exploit other aspects of the tag recommendation problem.Bibtex Citation@inproceedings{belem@sigir11, author = {Fabiano Bel\'em and Eder Martins and Jussara Almeida and Marcos Gon\c{c}alves}, title = {Associative Tag Recommendation Exploiting Multiple Textual Features}, booktitle = {{Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR'11)}}, month = {{July}}, year = {2011} }

The highest ranking YouTube channel about books in Ukraine was Peake-week Papers, with over 1.4 million user interactions in 2020. The video blog's owner Alisa talks about the works she read or planning to read. The second most popular channel was Tyoplaya Taverna (Теплая Таверна).

YouTube-BoundingBoxes (YT-BB) is a large-scale data set of video URLs with densely-sampled object bounding box annotations. The data set consists of approximately 380,000 video segments about 19s long, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often akin to that of a hand-held cell phone camera. The objects represent a subset of the MS COCO label set. All video segments were human-annotated with high-precision classification labels and bounding boxes at 1 frame per second.

The file contains the data collection used for the Conjoint Analysis - Study 2 of the article. If you want the same data in SAV I can provide it.

This dataset forms part of the work undertaken for the Wellcome Trust funded project ‘Orphan drugs: High prices, access to medicines and the transformation of biopharmaceutical innovation’ [219875/Z/19/Z]. It comprises of two .csv format files. The data were gathered using the University of Amsterdam Digital Methods Initiative’s ‘Data Tools for YouTube’ tool (DTFY) before being processed and extracted from Gephi (0.9.2) in line with approval form the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 040659) granted on 14-Jun-2021. The data include a list of 7,469 nodes and 72,9327 edges used within social network analyses of YouTube data around the term 'rare disease'.

Abstract This analytical paper proposes reflection on transformations of the television conversation operated on YouTube, with special interest in bodies that position themselves as dissidents. It takes as its empirical object three channels - Põe na Roda [Talk to All], Canal das Bee [Bee Channel] and Drag-se [Drag yourself] - that stress mediatic conventions of masculinities and femininities in relation to matrices of talk show and debate programs. The study points to places of reinforcement of the binary convention that marks the television bodies, despite the interest in identifying YouTube traffic places. It presents a historicized approach to the phenomenon, investing in the notion of television genre as a figure of historicities, in association with the sense of performance and self-reports.

Introduction

Digital Content Creation Statistics: Digital content creation encompasses the production of diverse media content for online consumption, spanning written articles, videos, podcasts, graphics, and interactive experiences.

With the proliferation of digital platforms and increased internet penetration, demand for engaging content is surging.

Video content remains dominant, with platforms like YouTube and TikTok driving engagement. While emerging formats like live streams and AR offer new engagement opportunities.

Content creators, platforms, agencies, and technology providers constitute the market landscape. Navigating challenges such as content saturation and algorithmic changes while capitalizing on opportunities in monetization models and emerging technologies like Generative AI and VR.

Understanding these dynamics is crucial for businesses and creators aiming to thrive in the competitive digital content space.

characteristics of published youtube video reviewsData extracted from published manuscripts, Excel spreadsheet with two tabs, one for the orginal sample and another for newer manuscripts. The row titled PMID indicates the PubMed ID number,and identifies that article that the data is taken from.DE on review methods V2.xls

Abstract This paper aims at analyzing video trails of two networks formed from two Youtube videos, related to the #YoSoy132 movement, occurred in 2012, in Mexico. These networks were analyzed using methods from Social Network Analysis (SNA), with the graphs visualized through the Gephi program. The networks showed very different connection patterns, one with more clusters less linked to each other, and another, an egocentric network example.