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.
We present a method for accurately predicting the long time popularity of online content from early measurements of user's access. Using two content sharing portals, Youtube and Digg, we show that by modeling the accrual of views and votes on content offered by these services we can predict the long-term dynamics of individual submissions from initial data. In the case of Digg, measuring access to given stories during the first two hours allows us to forecast their popularity 30 days ahead with remarkable accuracy, while downloads of Youtube videos need to be followed for 10 days to attain the same performance. The differing time scales of the predictions are shown to be due to differences in how content is consumed on the two portals: Digg stories quickly become outdated, while Youtube videos are still found long after they are initially submitted to the portal. We show that predictions are more accurate for submissions for which attention decays quickly, whereas predictions for evergreen content will be prone to larger errors. Viral videos that gain popularity through the process of Internet sharing are having a profound impact on society. Existing studies on viral videos have only been on small or confidential datasets. We collect by far the largest open benchmark for viral video study called CMU Viral Video Dataset, and share it with researchers from both academia and industry. Having verified existing observations on the dataset, we discover some interesting characteristics of viral videos. Based on our analysis, in the second half of the paper, we propose a model to forecast the future peak day of viral videos. The application of our work is not only important for advertising agencies to plan advertising campaigns and estimate costs, but also for companies to be able to quickly respond to rivals in viral marketing campaigns. The proposed method is unique in that it is the first attempt to incorporate video metadata into the peak day prediction. The empirical results demonstrate that the proposed method outperforms the state-of-the-art methods, with statistically significant differences. Content popularity prediction finds application in many areas, including media advertising, content caching, movie revenue estimation, traffic management and macro-economic trends forecasting, to name a few. However, predicting this popularity is difficult due to, among others, the effects of external phenomena, the influence of context such as locality and relevance to users,and the difficulty of forecasting information cascades. In this paper we identify patterns of temporal evolution that are generalisable to distinct types of data, and show that we can (1) accurately classify content based on the evolution of its popularity over time and (2) predict the value of the content's future popularity. We verify the generality of our method by testing it on YouTube, Digg and Vimeo data sets and find our results to outperform the K-Means baseline when classifying the behaviour of content and the linear regression baseline when predicting its popularity.