With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, they face the need of estimating the current and predicting the future content popularity. In this paper, we approach the problem of video popularity prediction not from the side of a video hosting service, as done in all previous studies, but from the side of an operating company, which provides a popular video search service that aggregates content from different video hosting websites. We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web. We show that video popularity prediction based on the embed and link data coupled with the internal search and browsing data significantly improves video popularity prediction based only on the data provided by the video hosting and can even adequately replace the API data in the cases when it is partly or completely unavailable.
The research described in @cite_3 was devoted to prediction of future video popularity in terms of the shares of a video in online social networks like Facebook. The analogous work was made in @cite_15 @cite_0 : they collected the share data not being inside the social network company, but by receiving them from end users. Similar study of sharing behavior were carried out for Twitter @cite_4 . Further studies concentrated on more sophisticated models and features like sentiments extracted from video frames and user comments @cite_34 @cite_17 . The described methods could not be used in our work because they use either the data that is not publicly available for third parties, or the APIs of those social platforms. It means that a search engine which would decide to rely on that data would need to at least use the APIs of those services, while the goal of this study is to show to what extent an operating company can be independent from any APIs, even from seemingly more important APIs of video hosting services.
@cite_4 @cite_3 @cite_0 @cite_15 @cite_34 @cite_17
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By combining multiple social media datasets, it is possible to gain insight into each dataset that goes beyond what could be obtained with either individually. In this paper we combine user-centric data from Twitter with video-centric data from YouTube to build a rich picture of who watches and shares what on YouTube. We study 87K Twitter users, 5.6 million YouTube videos and 15 million video sharing events from user-, video- and sharing-event-centric perspectives. We show that features of Twitter users correlate with YouTube features and sharing-related features. For example, urban users are quicker to share than rural users. We find a superlinear relationship between initial Twitter shares and the final amounts of views. We discover that Twitter activity metrics play more role in video popularity than mere amount of followers. We also reveal the existence of correlated behavior concerning the time between video creation and sharing within certain timescales, showing the time onset for a coherent response, and the time limit after which collective responses are extremely unlikely. Response times depend on the category of the video, suggesting Twitter video sharing is highly dependent on the video content. To the best of our knowledge, this is the first large-scale study combining YouTube and Twitter data, and it reveals novel, detailed insights into who watches (and shares) what on YouTube, and when. This paper reviews the state of the art and some emerging issues in research areas related to pattern analysis and monitoring of web-based social communities. This research area is important for several reasons. First, the presence of near-ubiquitous low-cost computing and communication technologies has enabled people to access and share information at an unprecedented scale. The scale of the data necessitates new research for making sense of such content. Furthermore, popular websites with sophisticated media sharing and notification features allow users to stay in touch with friends and loved ones; these sites also help to form explicit and implicit social groups. These social groups are an important source of information to organize and to manage multimedia data. In this article, we study how media-rich social networks provide additional insight into familiar multimedia research problems, including tagging and video ranking. In particular, we advance the idea that the contextual and social aspects of media are as important for successful multimedia applications as is the media content. We examine the inter-relationship between content and social context through the prism of three key questions. First, how do we extract the context in which social interactions occur? Second, does social interaction provide value to the media object? Finally, how do social media facilitate the repurposing of shared content and engender cultural memes? We present three case studies to examine these questions in detail. In the first case study, we show how to discover structure latent in the social media data, and use the discovered structure to organize Flickr photo streams. In the second case study, we discuss how to determine the interestingness of conversations—and of participants—around videos uploaded to YouTube. Finally, we show how the analysis of visual content, in particular tracing of content remixes, can help us understand the relationship among YouTube participants. For each case, we present an overview of recent work and review the state of the art. We also discuss two emerging issues related to the analysis of social networks—robust data sampling and scalable data analysis. While multimedia and social computing research have used crowdsourcing techniques to annotate objects, actions, and scenes in social video sites like YouTube, little work has addressed the crowdsourcing of personal and social traits in online social video or social media content in general. In this paper, we address the problems of (1) crowdsourcing the annotation of first impressions of video bloggers (vloggers) personal and social traits in conversational YouTube videos, and (2) mining the impressions with the goal of modeling the interplay of different vlogger facets. First, we design a human annotation task to crowdsource impressions of vloggers that extends a tradition of studies of personality impressions with the addition of attractiveness and mood impressions. Second, we propose a probabilistic framework using Topic Models to discover prototypical impressions that are data driven, and that combine multiple facets of vloggers. Finally, we address the task of automatically predicting topic impressions using nonverbal and verbal content extracted from videos and comments. Our study of 442 YouTube vlogs and 2,210 annotations collected in Mechanical Turk supports recent literature showing the feasibility to crowdsource interpersonal human impression with comparable quality to what is reported in social psychology research, and provides insights on the interplay among human first impressions. We also show that topic models are useful to discover meaningful prototypical impressions that can be validated by humans, and that different topics can be predicted using different sources of information from vloggers’ nonverbal and verbal content, as well as comments from the audience. Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.