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.
We build on these works by proposing a popularity prediction method for social media videos . We use computer vision algorithms to calculate visual features and verify if combining it with early evolution data can improve prediction accuracy for videos published online. Although recent works have also addressed the problem of online video analysis @cite_3 and popularity prediction @cite_28 from a multi-modal perspective, their focus is on micro-videos that last not more than a few seconds, while we consider longer videos . To the best of our knowledge, this is one of the first attempts to use this kind of features in the context of online video popularity prediction.
@cite_28 @cite_3
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Micro-videos, a new form of user generated contents (UGCs), are gaining increasing enthusiasm. Popular micro-videos have enormous commercial potential in many ways, such as online marketing and brand tracking. In fact, the popularity prediction of traditional UGCs including tweets, web images, and long videos, has achieved good theoretical underpinnings and great practical success. However, little research has thus far been conducted to predict the popularity of the bite-sized videos. This task is non-trivial due to three reasons: 1) micro-videos are short in duration and of low quality; 2) they can be described by multiple heterogeneous channels, spanning from social, visual, acoustic to textual modalities; and 3) there are no available benchmark dataset and discriminant features that are suitable for this task. Towards this end, we present a transductive multi-modal learning model. The proposed model is designed to find the optimal latent common space, unifying and preserving information from different modalities, whereby micro-videos can be better represented. This latent space can be used to alleviate the information insufficiency problem caused by the brief nature of micro-videos. In addition, we built a benchmark dataset and extracted a rich set of popularity-oriented features to characterize the popular micro-videos. Extensive experiments have demonstrated the effectiveness of the proposed model. As a side contribution, we have released the dataset, codes and parameters to facilitate other researchers. According to our statistics on over 2 million micro-videos, only 1.22 of them are associated with venue information, which greatly hinders the location-oriented applications and personalized services. To alleviate this problem, we aim to label the bite-sized video clips with venue categories. It is, however, nontrivial due to three reasons: 1) no available benchmark dataset; 2) insufficient information, low quality, and 3) information loss; and 3) complex relatedness among venue categories. Towards this end, we propose a scheme comprising of two components. In particular, we first crawl a representative set of micro-videos from Vine and extract a rich set of features from textual, visual and acoustic modalities. We then, in the second component, build a tree-guided multi-task multi-modal learning model to estimate the venue category for each unseen micro-video. This model is able to jointly learn a common space from multi-modalities and leverage the predefined Foursquare hierarchical structure to regularize the relatedness among venue categories. Extensive experiments have well-validated our model. As a side research contribution, we have released our data, codes and involved parameters.