In this work, we propose a regression method to predict the popularity of an online video measured by its number of views. Our method uses Support Vector Regression with Gaussian radial basis functions. We show that predicting popularity patterns with this approach provides more precise and more stable prediction results, mainly thanks to the nonlinear character of the proposed method as well as its robustness. We prove the superiority of our method against the state of the art using datasets containing almost 24 000 videos from YouTube and Facebook. We also show that using visual features, such as the outputs of deep neural networks or scene dynamics’ metrics, can be useful for popularity prediction before content publication. Furthermore, we show that popularity prediction accuracy can be improved by combining early distribution patterns with social and visual features and that social features represent a much stronger signal in terms of video popularity prediction than the visual ones.
Textual content, such as Twitter messages, Digg stories or online news, is typically distributed very fast and catches users' attention for a relatively short period of time @cite_22 . Its popularity, measured in number of user actions such as comments, re-tweets or likes, is therefore highly skewed and can be modelled, e.g. with log-normal distribution @cite_25 . Video content exhibits similar heavy-tailed distribution, while its popularity is typically measured by the number of views @cite_36 . The availability of the video content and related popularity data via the YouTube platform, where every minute over 100 hours of video is uploaded @cite_36 , researchers were able to investigate other aspects related to the video content distribution. The most representative topics include prediction of the peak popularity time of the video @cite_13 or identifying popularity evolution patterns @cite_11 . However, most if not all methods used to predict the popularity of a given video rely on its early evolution pattern @cite_32 @cite_24 @cite_0 or its social context @cite_4 . Contrary to the method proposed in this paper, they do not exploit additional visual cues to improve their prediction accuracy.
@cite_4 @cite_22 @cite_36 @cite_32 @cite_24 @cite_0 @cite_13 @cite_25 @cite_11
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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. Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content. 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. The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (“niche interests”) from those of more general interest to the user community. This distinction is useful for predicting a story’s eventual popularity from users’ early reactions to the story.