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.     

Catching a viral video

The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos—videos that become popular through internet sharing. In this paper we seek to better understand viral videos on YouTube by analyzing sharing and its relationship to video popularity using millions of YouTube videos. The socialness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link, Facebook referral) or non-social (e.g. a link from related videos). We find that viewership patterns of highly social videos are very different from less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. By using our insights on viral videos we are able develop a method for ranking blogs and websites on their ability to spread viral videos.     

Image and video disclosure of substance use on social media websites

The present study examines young adults’ use of social media websites, such as MySpace, Facebook, and YouTube, to post public images and videos of themselves depicting alcohol consumption, inebriated behavior, or recreational marijuana use. A content analysis revealed that the majority of image and video representations of alcohol consumption depicted females in social gatherings while images and videos depicting marijuana use depicted solitary males. Videos typically were viewed frequently and gained positive ratings. Among a sample of college students, one-third of participants reported having posted a picture depicting substance use on a social networking site, with 97% aware that others engage in this phenomenon. Students’ perceptions of alcohol-related postings were generally positive or seen as a matter of individual choice while marijuana-related postings were generally viewed more negatively.     

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.     

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.     

Share with strangers: Privacy bubbles as user-centered privacy control for mobile content sharing applications

A continually increasing number of pictures and videos is shared in online social networks. Current sharing platforms, however, only offer limited options to define who has access to the content. Users may either share it with individuals or groups from their social graph, or make it available to the general public. Sharing content with users to which no social ties exist, even if they were physically close to the places where content was created and witnessed the same event, is however not supported by most existing platforms. We thus propose a novel approach to share content with such users based on so-called privacy bubbles. Privacy bubbles metaphorically represent the private sphere of the users and automatically confine the access to the content generated by the bubble creator to people within the bubble. Bubbles extend in both time and space, centered around the collection time and place, and their size can be adapted to the user's preferences. We confirm the user acceptance of our concept through a questionnaire-based study with 175 participants, and a prototype implementation shows the technical feasibility of our scheme.     

With a View to the Side: YouTube’s Sidebar and YouTuber’s Linguistic Style as Hints for Trust-Related Evaluations

Based on a heuristic approach to information processing, thematic reference of YouTube’s sidebars and YouTuber’s linguistic style are viewed as cues that should impact viewers’ evaluation of videos. In this study, a 2 × 2 online experiment was conducted wherein these factors were varied systematically for a video about nutrition myths. 147 participants assessed the credibility of information, YouTuber’s trustworthiness, and the self-reported learning gain. Results showed that a sidebar referring to similar (vs. unrelated) videos increased participants’ perceived trustworthiness of a YouTuber who used a YouTube-typical (vs. formal) language. Moreover, participants judged the learning gain to be higher when YouTube’s sidebar referred to similar videos. However, thematic reference of sidebar and linguistic style did not impact participants’ credibility judgments. Since people seem to recognize YouTube’s sidebars when evaluating videos, YouTube’s recommendation criteria might not only mediate videos, but also influence people’s judgments of YouTubers and videos.     

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.     

Value-added service providers for mobile education: empirical challenges and analytics

More people have access to Multimedia Messaging Service (MMS, a.k.a. mobile picture messaging) than to the Internet, but mobile education markets have yet to adopt MMS as a content delivery mechanism. This paper investigates the role of carrier interoperability as an enabler of MMS in mobile multimedia distance learning. Using instructor reuse of content and learner access to content as feasibility criteria, we empirically evaluate the performance, user adoption, and commercial market of MMS-based mobile education. This study deployed a value-added service that broadcasts videos via MMS to cell phones, and conducted a 9-month public education campaign with weekly broadcasts on breast cancer. We selected a video format and markup language that is compatible with domestic carriers and cell phones, and supports existing educational material. To contrast behaviors between participants with and without access to the Internet, we offered participants the same content via MMS, email and the Web. 277 participants enrolled in the campaign; 120 opted to receive the videos via mobile messaging, and 157 had Internet access and opted to receive videos via email or the Web. Campaign analytics reveal that all participants without Internet access successfully received the MMS video broadcasts, and significantly, one-third of participants with Internet access opted to receive the videos via MMS as well. We conclude with a discussion of why participants with Internet access may have chosen MMS over Internet-based alternatives. We also estimate the size of the market for MMS-based mobile education, and distinguish it from the person-to-person messaging market. This research is beneficial to educators targeting diverse demographics and education disparities, and to mobile commerce economists evaluating emerging markets.     

Older people’s production and appropriation of digital videos: an ethnographic study

While most of today’s children, young people, and adults are both consumers and producers of digital content, very little is known about older people as digital content creators. Drawing on a three-year ethnographic study, this paper reports on the digital video production and appropriation of approximately 200 older people (aged 60–85). They generated 320 videos over the course of the study. We show their motivations for engaging in digital video production, discuss their planned video making, and highlight their creativity while editing videos. We show the different meanings they ascribed to digital videos in their social appropriation of these objects, the meaningful strategies they adopted to share videos, and the impact on their perceived wellbeing. Furthermore, we outline the solutions the participants developed to overcome or cope with interaction issues they faced over time. We argue that the results portray older people as active and creative makers of digital videos with current video capturing, editing, and sharing technologies. We contend that this portrayal both encourages us to re-consider how older people should be seen within human–computer interaction and helps to frame future research/design activities that bridge the grey digital divide.     

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.     

On the tag localization of web video

Nowadays, numerous social videos have pervaded on the web. Social web videos are characterized with the accompanying rich contextual information which describe the content of videos and thus greatly facilitate video search and browsing. Generally, those contextual data such as tags are provided at the whole video level, without temporal indication of when they actually appear in the video, let alone the spatial annotation of object related tags in the video frames. However, many tags only describe parts of the video content. Therefore, tag localization, the process of assigning tags to the underlying relevant video segments or frames even regions in frames is gaining increasing research interests and a benchmark dataset for the fair evaluation of tag localization algorithms is highly desirable. In this paper, we describe and release a dataset called DUT-WEBV, which contains about 4,000 videos collected from YouTube portal by issuing 50 concepts as queries. These concepts cover a wide range of semantic aspects including scenes like “mountain”, events like “flood”, objects like “cows”, sites like “gas station”, and activities like “handshaking”, offering great challenges to the tag (i.e., concept) localization task. For each video of a tag, we carefully annotate the time durations when the tag appears in the video and also label the spatial location of object with mask in frames for object related tag. Besides the video itself, the contextual information, such as thumbnail images, titles, and YouTube categories, is also provided. Together with this benchmark dataset, we present a baseline for tag localization using multiple instance learning approach. Finally, we discuss some open research issues for tag localization in web videos.     

The lesbian,gay, bisexual and transgender community online: discussions of bullying and self-disclosure in YouTube videos

Computer-mediated communication has become a popular platform for identity construction and experimentation as well as social interaction for those who identify as lesbian, gay, bisexual or transgender (LGBT). The creation of user-generated videos has allowed content creators to share experiences on LGBT topics. With bullying becoming more common amongst LGBT youth, it is important to obtain a greater understanding of this phenomenon. In our study, we report on the analysis of 151 YouTube videos which were identified as having LGBT- and bullying-related content. The analysis reveals how content creators openly disclose personal information about themselves and their experiences in a non-anonymous rhetoric with an unknown public. These disclosures could indicate a desire to seek friendship, support and provide empathy.     

Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.     

On application-unbiased benchmarking of web videos from a social network perspective

Along with the emerging focus of community-contributed videos on the web, there is a strong demand of a well-designed web video benchmark for the research of social network based video content analysis. The existing video datasets are challenged in two aspects: (1) as the data resource, most of them are narrowed for a specific task, either focusing on one content analysis task with limited scales, or focusing on the pure social network analysis without downloading video content. (2) as the evaluation platform, few of them pay attention to the potential bias introduced by the sampling criteria, therefore cannot fairly measure the task performance. In this paper, we release a large-scale web video benchmark named MCG-WEBV 2.0, which crawls 248,887 YouTube videos and their corresponding social network structure with 123,063 video contributors. MCG-WEBV 2.0 can be used to explore the fusion between content and network for several web video analysis tasks. Based on MCG-WEBV 2.0, we further explore the sampling bias lies in web video benchmark construction. While sampling a completely unbiased video benchmark from million-scale collection is unpractical, we propose a task-dependent measurement of such bias, which minimizes the correlation between the potential video sampling bias and the corresponding content analysis task, if such bias is unavoidable. Following this principle, we have shown several exemplar application scenarios in MCG-WEBV 2.0.     

Designing for video: investigating the contextual cues within viewing situations

The viewing of video increasingly occurs in a wide range of public and private environments via a range of static and mobile devices. The proliferation of content on demand and the diversity of the viewing situations means that delivery systems can play a key role in introducing audiences to contextually relevant content of interest whilst maximising the viewing experience for individual viewers. However, for video delivery systems to do this, they need to take into account the diversity of the situations where video is consumed, and the differing viewing experiences that users desire to create within them. This requires an ability to identify different contextual viewing situations as perceived by users. This paper presents the results from a detailed, multi-method, user-centred field study with 11 UK-based users of video-based content. Following a review of the literature (to identify viewing situations of interest on which to focus), data collection was conducted comprising observation, diaries, interviews and self-captured video. Insights were gained into whether and how users choose to engage with content in different public and private spaces. The results identified and validated a set of contextual cues that characterise distinctive viewing situations. Four archetypical viewing situations were identified: ‘quality time’, ‘opportunistic planning’, ‘sharing space but not content’ and ‘opportunistic self-indulgence’. These can be differentiated in terms of key contextual factors: solitary/shared experiences, public/private spaces and temporal characteristics. The presence of clear contextual cues provides the opportunity for video delivery systems to better tailor content and format to the viewing situation or additionally augment video services through social media in order to provide specific experiences sensitive to both temporal and physical contexts.     

Reality Television as a Model for Online Behavior: Blogging, Photo, and Video Sharing

This research explores traditional mass media as an antecedent to nondirected self-disclosure online. New Internet-based tools allow users to communicate with global audiences, and to make intimate personal information available to this audience. At the same time, a culture that rewards the public performance of private thoughts and emotions is increasingly evident in "reality" television (RTV) programming. This study used survey data to examine RTV consumption, authoritarianism, and users' offline social context as potential antecedents for nondirected self-disclosure via blogs, online photo sharing, and online video sharing. RTV consumption correlated with blogging and video sharing, but not photo sharing. Social support network size was a significant correlate of photo sharing, indicating that photo sharing may be a more relational activity.     

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.     

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.