How YouTube videos are discovered and its impact on video views

As the largest video sharing site around the world, YouTube has been changing the way people entertain, gain popularity, and advertise. Discovering the major sources that drive views to a video and understanding how they impact the view growth pattern have become interesting topics for researchers as well as advertisers, media companies, or anyone who wish to have a shortcut to stardom. The work of this paper is to identify three major view sources, related video recommendation, YouTube search, and video highlight such as popular video list on YouTube homepage or video embedding on social networking sites, and examine the patterns of views from each view source. First, the impact of each view source on the view diversity and on the view share of each individual video is analyzed. It is found that while search and highlight create an effect of rich-get-richer, the related video recommendation equalizes the view distribution and helps users find niche videos. Second, the contribution of the three view sources to video popularity growth is investigated. The investigation reveals that search and related video recommendation are the two major sources that persistently drive views to a video. The view rates from recommendation and search are generally stabilized to be constant view rates. Third, the underlying factors that affect the long-term view rate from referrer videos are explored. The results indicate that the top referrer video set of a video is fairly stable and the view rate from recommendation is mainly determined by view rates of top referrer videos. Finally, whether highlight increases the view rate of a video after the duration of promotion is studied. The observations suggest that video highlight does not directly impact the view rate of a video after the event finishes. The findings presented in the paper provide several key insights into the impact and patterns of view contributions for each major source of the video views.     

Workload generation for YouTube

This paper introduces a workload characterization study of the most popular short video sharing service of Web 2.0, YouTube. Based on a vast amount of data gathered in a five-month period, we analyzed characteristics of around 250,000 YouTube popular and regular videos. In particular, we collected lists of related videos for each video clip recursively and analyzed their statistical behavior. Understanding YouTube traffic and similar Web 2.0 video sharing sites is crucial to develop synthetic workload generators. Workload simulators are required for evaluating the methods addressing the problems of high bandwidth usage and scalability of Web 2.0 sites such as YouTube. The distribution models, in particular Zipf-like behavior of YouTube popular video files suggests proxy caching of YouTube popular videos can reduce network traffic and increase scalability of YouTube Web site. YouTube workload characteristics provided in this work enabled us to develop a workload generator to evaluate the effectiveness of this approach.     

Documenting and sharing software knowledge using screencasts

Screencasts are used to capture a developer’s screen while they narrate how a piece of software works or how the software can be extended. They have recently become a popular alternative to traditional text-based documentation. This paper describes our investigation into how developers produce and share developer-focused screencasts. In this study, we identified and analyzed a set of development screencasts from YouTube to explore what kinds of software knowledge are shared in video walkthroughs of code and what techniques are used for sharing software knowledge. We also interviewed YouTube screencast producers to understand their motivations for creating screencasts as well as to discover the challenges they face while producing code-focused videos. Finally, we compared YouTube screencasts to videos hosted on the professional RailsCasts website to better understand the differences and practices of this more curated ecosystem with the YouTube platform. Our three-phase study showed that video is a useful medium for communicating program knowledge between developers and that developers build their online persona and reputation by sharing videos through social channels. These findings led to a number of best practices for future screencast creators.     

A unified framework for web video topic discovery and visualization

Together with the explosive growth of web video in sharing sites like YouTube, automatic topic discovery and visualization have become increasingly important in helping to organize and navigate such large-scale videos. Previous work dealt with the topic discovery and visualization problem separately, and did not take fully into account of the distinctive characteristics of multi-modality and sparsity in web video features. This paper tries to solve web video topic discovery problem with visualization under a single framework, and proposes a Star-structured K-partite Graph based co-clustering and ranking framework, which consists of three stages: (1) firstly, represent the web videos and their multi-model features (e.g., keyword, near-duplicate keyframe, near-duplicate aural frame, etc.) as a Star-structured K-partite Graph; (2) secondly, group videos and their features simultaneously into clusters (topics) and organize the generated clusters as a linked cluster network; (3) finally, rank each type of nodes in the linked cluster network by “popularity” and visualize them as a novel interface to let user interactively browse topics in multi-level scales. Experiments on a YouTube benchmark dataset demonstrate the flexibility and effectiveness of our proposed framework.     

Publicly Private and Privately Public: Social Networking on YouTube

YouTube is a public video-sharing website where people can experience varying degrees of engagement with videos, ranging from casual viewing to sharing videos in order to maintain social relationships. Based on a one-year ethnographic project, this article analyzes how YouTube participants developed and maintained social networks by manipulating physical and interpretive access to their videos. The analysis reveals how circulating and sharing videos reflects different social relationships among youth. It also identifies varying degrees of "publicness" in video sharing. Some participants exhibited "publicly private" behavior, in which video makers' identities were revealed, but content was relatively private because it was not widely accessed. In contrast, "privately public" behavior involved sharing widely accessible content with many viewers, while limiting access to detailed information about video producers' identities.     

A dynamic caching algorithm based on internal popularity distribution of streaming media

Most proxy caches for streaming videos do not cache the entire video but only a portion of it. This is partly due to the large size of video objects. Another reason is that the popularity of different parts of a video can be different, e.g., the prefix is generally more popular. Therefore, the development of efficient cache mechanisms requires an understanding of the internal popularity characteristics of streaming videos. This paper has two major contributions. Firstly, we analyze two 6-month long traces of RTSP video requests recorded at different streaming video servers of an entertainment video-on-demand provider, and show that the traces provide evidence that the internal popularity of the majority of the most popular videos obeys a k-transformed Zipf-like distribution. Secondly, we propose a caching algorithm which exploits this empirical internal popularity distribution. We find that this algorithm has similar performance compared with fine-grained caching but requires significantly less state information.     

YouTube and intergenerational communication: the case of Geriatric1927

This paper presents a case study of a 79 year old video blogger called ‘Geriatric1927’, and his use of the video sharing website, YouTube. Analysis of his first eight video blogs, and the subsequent text responses, reveals opportunities of this medium for intergenerational contact, reminiscence, reciprocal learning and co-creation of content, suggesting that older people can be highly motivated to use computers for social contact. The paper concludes by noting the importance of technologies that are socially engaging and meaningful for older people, and pointing to ways in which the social life of YouTube might be better promoted on its interface.

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

IFCS: an intelligent fast channel switching in IPTV over PON based on human behavior prediction

In this work we are concerned with detecting non-collaborative videos in video sharing social networks. Specifically, we investigate how much visual content-based analysis can aid in detecting ballot stuffing and spam videos in threads of video responses. That is a very challenging task, because of the high-level semantic concepts involved; of the assorted nature of social networks, preventing the use of constrained a priori information; and, which is paramount, of the context-dependent nature of non-collaborative videos. Content filtering for social networks is an increasingly demanded task: due to their popularity, the number of abuses also tends to increase, annoying the user and disrupting their services. We propose two approaches, each one better adapted to a specific non-collaborative action: ballot stuffing, which tries to inflate the popularity of a given video by giving “fake” responses to it, and spamming, which tries to insert a non-related video as a response in popular videos. We endorse the use of low-level features combined into higher-level features representation, like bag-of-visual-features and latent semantic analysis. Our experiments show the feasibility of the proposed approaches.     

Breaking news: “I have an eating disorder.” Video testimonials on YouTube

Over the last several decades there has been an exponential increase in the usage of the Internet, and social networking websites in particular. Social networking websites have gained popularity because they allow people to network on both a personal and professional level. The rise of testimonial videos about one’s experience with hardships has gained popularity as another way for people to connect with one another for support. The current study looks at men and women who utilized YouTube, a video posting website, to document their struggles with eating disorders (ED). Fifty videos were viewed and analyzed regarding content and viewer response. It was found that most posters actively sought treatment for their ED, yet sought out additional support on the Internet while also offering support for others. In addition, viewers responded with an overwhelmingly large number of supportive comments compared to negative comments (8:1).     

Automated Delineation of Subgroups in Web Video: A Medical Activism Case Study

Web 2.0 tools in general, and Web video in particular, provide new ways for activists to express their viewpoints to a broad audience. In this paper we deployed tools that have been used to find subgroups automatically in social networks and applied them to the problem of distinguishing between two sides of a controversial issue based on patterns of online interaction. We explored the problem of distinguishing between anti‐ and pro‐vaccination activists based on a social network of videos and associated comments posted on YouTube. Videos for the analysis were selected by submitting the term “vaccination” to a search on YouTube. A content analysis of the selected videos was then performed ( Keelan et al, 2007 ) to classify videos as pro‐ or anti‐vaccination. Then, a modified version of the SCAN method ( Chin and Chignell, 2008 ) for identifying cohesive subgroups in social networks was applied to the social network inferred from the discussions about the videos. Results showed that a cohesive subgroup of anti‐vaccination people existed in discussions around anti‐vaccination videos, whereas discussions around pro‐vaccination videos included both anti‐vaccination and pro‐vaccination people. Implications of the method and results for more general delineation of types of medical activism and the opposing camps within those camps are discussed.     

Characteristics of YouTube network traffic at a campus network – Measurements,models, and implications

User-Generated Content has become very popular since new web services such as YouTube allow for the distribution of user-produced media content. YouTube-like services are different from existing traditional VoD services in that the service provider has only limited control over the creation of new content. We analyze how content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. Based on these measurements, we analyzed the duration and the data rate of streaming sessions, the popularity of videos, and access patterns for video clips from the clients in the campus network. The analysis of the traffic shows that trace statistics are relatively stable over short-term periods while long-term trends can be observed. We demonstrate how synthetic traces can be generated from the measured traces and show how these synthetic traces can be used as inputs to trace-driven simulations. We also analyze the benefits of alternative distribution infrastructures to improve the performance of a YouTube-like VoD service. The results of these simulations show that P2P-based distribution and proxy caching can reduce network traffic significantly and allow for faster access to video clips.     

基于文本扩展模型的网络视频聚类方法

随着视频分享网站的兴起和快速发展,互联网上的视频数量呈爆炸式增长,对视频的组织及分类成为视频有效使用的基础。视频聚类技术由于只需要考虑视频数据内在的簇结构、不需要人工干预,越来越受到人们的青睐。现有的视频聚类方法有基于视频关键帧视觉相似性的方法、基于视频标题文本聚类的方法、文本和视觉多模态融合的方法。基于视频标题文本聚类的视频聚类方法由于其简便性与高效性而被企业界广泛使用,但视频标题由于其短文本的语义稀疏特性,聚类效果欠佳。为此,本文面向社会媒体视频,提出了一种社会媒体平台上视频相关多源文本融合的视频聚类方法,以克服由于视频标题的短文本带来的语义稀疏问题。不同文本聚类算法上的实验结果证明了多源文本数据融合方法的有效性。     

Real-Time Near-Duplicate Elimination for Web Video Search With Content and Context

With the exponential growth of social media, there exist huge numbers of near-duplicate web videos, ranging from simple formatting to complex mixture of different editing effects. In addition to the abundant video content, the social Web provides rich sets of context information associated with web videos, such as thumbnail image, time duration and so on. At the same time, the popularity of Web 2.0 demands for timely response to user queries. To balance the speed and accuracy aspects, in this paper, we combine the contextual information from time duration, number of views, and thumbnail images with the content analysis derived from color and local points to achieve real-time near-duplicate elimination. The results of 24 popular queries retrieved from YouTube show that the proposed approach integrating content and context can reach real-time novelty re-ranking of web videos with extremely high efficiency, where the majority of duplicates can be rapidly detected and removed from the top rankings. The speedup of the proposed approach can reach 164 times faster than the effective hierarchical method proposed in , with just a slight loss of performance.     

Learning automatic concept detectors from online video

Concept detection is targeted at automatically labeling video content with semantic concepts appearing in it, like objects, locations, or activities. While concept detectors have become key components in many research prototypes for content-based video retrieval, their practical use is limited by the need for large-scale annotated training sets. To overcome this problem, we propose to train concept detectors on material downloaded from web-based video sharing portals like YouTube, such that training is based on tags given by users during upload, no manual annotation is required, and concept detection can scale up to thousands of concepts. On the downside, web video as training material is a complex domain, and the tags associated with it are weak and unreliable. Consequently, performance loss is to be expected when replacing high-quality state-of-the-art training sets with web video content.This paper presents a concept detection prototype named TubeTagger that utilizes YouTube content for an autonomous training. In quantitative experiments, we compare the performance when training on web video and on standard datasets from the literature. It is demonstrated that concept detection in web video is feasible, and that – when testing on YouTube videos – the YouTube-based detector outperforms the ones trained on standard training sets. By applying the YouTube-based prototype to datasets from the literature, we further demonstrate that: (1) If training annotations on the target domain are available, the resulting detectors significantly outperform the YouTube-based tagger. (2) If no annotations are available, the YouTube-based detector achieves comparable performance to the ones trained on standard datasets (moderate relative performance losses of 11.4% is measured) while offering the advantage of a fully automatic, scalable learning. (3) By enriching conventional training sets with online video material, performance improvements of 11.7% can be achieved when generalizing to domains unseen in training.     

Characterizing and modelling popularity of user-generated videos

This paper develops a framework for studying the popularity dynamics of user-generated videos, presents a characterization of the popularity dynamics, and proposes a model that captures the key properties of these dynamics. We illustrate the biases that may be introduced in the analysis for some choices of the sampling technique used for collecting data; however, sampling from recently-uploaded videos provides a dataset that is seemingly unbiased. Using a dataset that tracks the views to a sample of recently-uploaded YouTube videos over the first eight months of their lifetime, we study the popularity dynamics. We find that the relative popularities of the videos within our dataset are highly non-stationary, owing primarily to large differences in the required time since upload until peak popularity is finally achieved, and secondly to popularity oscillation. We propose a model that can accurately capture the popularity dynamics of collections of recently-uploaded videos as they age, including key measures such as hot set churn statistics, and the evolution of the viewing rate and total views distributions over time.     

Cost-effective capacity migration of Peer-to-Peer social media to clouds

Social media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with CDN (Content Delivery Network)- based engines, but they suffer from excessive costs for deploying dedicated servers. And with the further expansions on network traffic of social media streaming, a cost-effective solution remains an illusive goal. The emergence of cloud computing sets out to meet the challenge by dynamically leasing cloud servers. This paper aims to realize the capacity migration of social media systems to clouds at the reduced cost. Firstly, by lowering the capacity requested from clouds to reduce the capacity migration cost. Based on the crawled data from YouTube which is the most representative online social media, we find that with larger than 90% probability, the YouTube user’s all requested videos are within three hops of related videos. Then the three hops of related videos are regarded as a cluster and a user’s request can be partly satisfied by other users who watch videos in the same cluster to lessen the capacity requested from clouds. Therefore the capacity migration for clusters is under the P2P (Peer-to-Peer) paradigm and a cloud-assisted P2P social media system is proposed. Secondly, given the diverse capacities, cost, limited lease size of cloud servers, we formulate an optimization problem about how to lease cloud servers to minimize the leasing cost and a heuristic solution is presented. The evaluation based on the crawled data from a cluster of YouTube videos shows the efficiency of the proposed schemes.     

How useful is social feedback for learning to rank YouTube videos?

A vast amount of social feedback expressed via ratings (i.e., likes and dislikes) and comments is available for the multimedia content shared through Web 2.0 platforms. However, the potential of such social features associated with shared content still remains unexplored in the context of information retrieval. In this paper, we first study the social features that are associated with the top-ranked videos retrieved from the YouTube video sharing site for the real user queries. Our analysis considers both raw and derived social features. Next, we investigate the effectiveness of each such feature for video retrieval and the correlation between the features. Finally, we investigate the impact of the social features on the video retrieval effectiveness using state-of-the-art learning to rank approaches. In order to identify the most effective features, we adopt a new feature selection strategy based on the Maximal Marginal Relevance (MMR) method, as well as utilizing an existing strategy. In our experiments, we treat popular and rare queries separately and annotate 4,969 and 4,949 query-video pairs from each query type, respectively. Our findings reveal that incorporating social features is a promising approach for improving the retrieval performance for both types of queries.     

Efficient indexing and retrieval of large-scale geo-tagged video databases

We are witnessing a significant growth in the number of smartphone users and advances in phone hardware and sensor technology. In conjunction with the popularity of video applications such as YouTube, an unprecedented number of user-generated videos (UGVs) are being generated and consumed by the public, which leads to a Big Data challenge in social media. In a very large video repository, it is difficult to index and search videos in their unstructured form. However, due to recent development, videos can be geo-tagged (e.g., locations from GPS receiver and viewing directions from digital compass) at the acquisition time, which can provide potential for efficient management of video data. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). This effectively converts a challenging video management problem into a spatial database problem. This paper attacks the challenges of large-scale video data management using spatial indexing and querying of FOVs, especially maximally harnessing the geographical properties of FOVs. Since FOVs are shaped similar to slices of pie and contain both location and orientation information, conventional spatial indexes, such as R-tree, cannot index them efficiently. The distribution of UGVs’ locations is non-uniform (e.g., more FOVs in popular locations). Consequently, even multilevel grid-based indexes, which can handle both location and orientation, have limitations in managing the skewed distribution. Additionally, since UGVs are usually captured in a casual way with diverse setups and movements, no a priori assumption can be made to condense them in an index structure. To overcome the challenges, we propose a class of new R-tree-based index structures that effectively harness FOVs’ camera locations, orientations and view-distances, in tandem, for both filtering and optimization. We also present novel search strategies and algorithms for efficient range and directional queries on our indexes. Our experiments using both real-world and large synthetic video datasets (over 30 years’ worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms.     

CPA-VoD: Cloud and Peer-Assisted Video on Demand System for Mobile Devices

With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these devices aggravate the network pressure of content providers. Previous studies use CDN to solve this problem. But its static leasing mechanism in which the rental space cannot be dynamically adjusted makes the operational cost soar and incompatible with the dynamically video delivery. In our study, based on a thorough analysis of user behavior from Tencent Video, a popular Chinese on-line video share platform, we identify two key user behaviors. Firstly, lots of users in the same region tend to watch the same video. Secondly, the popularity distribution of videos conforms with the Pareto principle, i.e., the top 20% popular videos own 80% of all video traffic. To turn these observations into silver bullet, we propose and implement a novel cloud- and peer-assisted video on demand system (CPA-VoD). In the system, we group users in the same region as a peer swarm, and in the same peer swarm, users can provide videos to other users by sharing their cached videos. Besides, we cache the 10% most popular videos in cloud servers to further alleviate the network pressure. We choose cloud servers to cache videos because the rental space can be dynamically adjusted. According to the evaluation on a real dataset from Tencent Video, CPA-VoD alleviates the network pressure and the operation cost excellently, while only 20.9% traffic is serviced by the content provider.