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.
Prediction of web content (not only video) popularity is a well known and widely investigated problem @cite_9 . Prediction methods similar to the ones described in the previous subsection were applied to predict popularity of web pages in general @cite_19 @cite_12 @cite_14 and for popularity of news measured in comments count @cite_6 . The popularity of tweets in terms of the number of retweets and shows were studied in @cite_26 @cite_13 @cite_23 .
@cite_14 @cite_26 @cite_9 @cite_6 @cite_19 @cite_23 @cite_13 @cite_12
2037970810 2000139610 2061887386 2506342487
Predicting Web content popularity is an important task for supporting the design and evaluation of a wide range of systems, from targeted advertising to effective search and recommendation services. We here present two simple models for predicting the future popularity of Web content based on historical information given by early popularity measures. Our approach is validated on datasets consisting of videos from the widely used YouTube video-sharing portal. Our experimental results show that, compared to a state-of-the-art baseline model, our proposed models lead to significant decreases in relative squared errors, reaching up to 20 reduction on average, and larger reductions (of up to 71 ) for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity. In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5 6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2 3 days. Previous research on online media popularity prediction concluded that the rise in popularity of online videos maintains a conventional logarithmic distribution. However, recent studies have shown that a significant portion of online videos exhibit bursty sudden rise in popularity, which cannot be accounted for by video domain features alone. In this paper, we propose a novel transfer learning framework that utilizes knowledge from social streams (e.g., Twitter) to grasp sudden popularity bursts in online content. We develop a transfer learning algorithm that can learn topics from social streams allowing us to model the social prominence of video content and improve popularity predictions in the video domain. Our transfer learning framework has the ability to scale with incoming stream of tweets, harnessing physical world event information in real-time. Using data comprising of 10.2 million tweets and 3.5 million YouTube videos, we show that social prominence of the video topic (context) is responsible for the sudden rise in its popularity where social trends have a ripple effect as they spread from the Twitter domain to the video domain. We envision that our cross-domain popularity prediction model will be substantially useful for various media applications that could not be previously solved by traditional multimedia techniques alone. Recent years have witnessed the prosperity of a new type of real-time user-generated comment, or so-called DanMu, in many recent online video platforms. These DanMu-enabled video platforms present scrolling marquee comments overlaid directly on top of the videos by synchronizing these comments to specific playback times. In this paper, we study the prediction of video popularity in these platforms, which may benefit a lot of applications ranging from online advertising for website holders to popular video recommendation for audiences. Different from traditional online video platforms where only traditional reviews are available, these DanMus make viewers easily see other viewers' opinions and communicate with each other in a much more direct way. Consequently, viewers are easily influenced by others' behaviors over time, which is considered as the herding effect in social science. However, how to address the unique characteristics i.e., the herding effect of DanMu-enabled online videos for more accurate popularity prediction is still under-explored. To that end, in this paper, we first explore and measure the herding effect of DanMu-enabled video popularity from multiple aspects, including the popular videos, the popular DanMus and the newly updated videos. Also, we recognize that the uploaders' influence and video quality affect the video popularity as well. Along this line, we propose a model that incorporates the herding effect, uploaders' influence and video quality for predicting the video popularity. An effective estimation method is also proposed. Finally, experimental results on real-world data show that our proposed prediction model improves the prediction accuracy by 47.19i?ź compared to the baselines.