1 / 20

Cross-Network Association for YouTube Video Promotion: A Three-Stage Framework

This study delves into the challenges faced by YouTube videos in reaching wider audiences and proposes a framework leveraging Twitter for enhanced video dissemination. The research includes a comprehensive analysis of YouTube video popularity dynamics, the role of Twitter in promoting videos, and a three-stage framework combining heterogeneous topic modeling and association mining techniques to identify optimal Twitter followees for maximizing video dissemination. The study also explores user-perceived interestness, virtual cost, and properness metrics to determine the most effective promotional strategies. The proposed framework aims to enhance collaboration between YouTube and Twitter, ultimately improving the visibility and reach of YouTube videos in the digital landscape.

griffen
Download Presentation

Cross-Network Association for YouTube Video Promotion: A Three-Stage Framework

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mining Cross-network Association for YouTube Video Promotion Ming Yan Institute of Automation, Chinese Academy of Sciences May 15, 2014

  2. Outline • Motivation • Three-stageFramework • Some Visualization • Further Discussion

  3. Background • Large quantities of videos are consumed in YouTube and the trend is growing year by year. • More than 1 billion unique users visit YouTube each month. • Over 6 billion hours of video are watched each month on YouTube. • 100 hours of video are uploaded to YouTube every minute. • YouTube exhibits limited propagation efficiency and many videos remain unknown to the wide public. • Long tail effect for the video view count distribution. • Short active life span for most videos.

  4. Background • YouTube video popularity limited by its internal mechanism. • Internal search • Related video recommendation • Channel subscription • Front page highlight • External referrers such as social media websites arise to be important sources to lead users to YouTube videos. • Twitter has been quickly growing as the top referrer source for web video discovery.

  5. YouTube video Motivation • For specific YouTube video, to identify proper Twitter followees with goal to maximize video dissemination to the followers. Twitter followee watch Got 1 billion views in 5 months Twitter follower

  6. Challenge • The heterogeneous knowledge association between YouTube video and Twitter followee • user-perceived • How to define the “properness” of candidate Twitter followee for a specific YouTube video • interestness • virtual cost Our Twitter followee identification scheme actually expects to find the optimal Twitter followee whose followers are more likely to show interest to the target video.

  7. User-perceived Solution • Illustration example better promotion referrer follow follow User Association favor view view

  8. Framework • Three Stages

  9. Heterogeneous Topic Modeling Twitter users ACM Multimedia 2014 @acmmm14 NBA @NBA • Input • YouTube video : [] • Twitter users with their follower set • Output • Twitter user distribution • YouTube user distribution Following Bill Gates @BillGates Britney Spears @britneyspears LDA Username @TwitterID … Twitter user distribution … • Topic Modeling Approach • On YouTube Side: • Propose an inverse Corr-LDA model to discover the YouTube video multimodal topics. YouTube video distribution … • On Twitter Side: • Standard LDA on Twitter followee-follower social graph. • user as document • user’s followees as word iCorr-LDA YouTube videos

  10. Cross-network Topic Association overlapped users • Input • Twitter user and video distribution • and (output of stage 1) • YouTube, Twitter and the overlapped user set • YouTube user interested video set • Output • Distribution transfer function • (: the aggregated YouTube user distribution) Association Mining Interested videos Aggregation … username YouTube user distribution • Approach • YouTube User Aggregation • Association Mining

  11. Cross-network Topic Association • YouTube User Aggregation user ’s interested videos … : the total number of keyframes and words in video : the total number of keyframes and words in ’s video set

  12. Cross-network Topic Association • Association Mining • Goal: • To obtain the association between the YouTube video space and Twitter user space. (i.e. ) • Approach: • Transition Probability-based Association • Regression-based Association • Latent Attribute-based Association overlapped users Explicit association/transition matrix: Association Mining

  13. Cross-network Topic Association • Transition Probability-based Association • Regression-based Association The overlapped users’ distribution matrix in Twitter and YouTube q=1: lasso problem and can be effectively solved by LARS and feature sign algorithm q=2: ridge regression problem and with analytical solution as

  14. Cross-network Topic Association • Latent Attribute-based Association (non-linear) • only on overlapped users • on all users • Innovation: To discover shared latent structure behind the two topic spaces. (After projected to the latent attribute spaces, user’s YouTube and Twitter distribution share the same coefficient.) shared latent user attribute • Only on overlapped users By some simple transfer, it can be efficiently solved by the sparse coding algorithm.

  15. Cross-network Topic Association • Latent attribute discovery on all users (plenty of non-overlapped users are considered in this scheme) • Objective function • Iteratively solved via three sub-problems

  16. Referrer Identification test YouTube video • Input • Distribution transfer function • Test videos • Twitter followee set Distribution Transfer • Output • Twitter followee rank for each video Matching • Approach • Direct product-based matching • Weighted product-based matching … candidate Twitter followees

  17. Referrer Identification • Direct product-based matching • Weighted product-based matching • Ranking SVM algorithm is used to train the weights: • Feature: • Training label: a designed properness score • With the learnt model parameter In charge of the coverage of the interested audiences In charge of the virtual cost

  18. Some Visualization

  19. Further Discussion • Some Extensible Application • Examining the value of Twitter followees(Our work can be viewed as valuing Twitter followee w.r.t. promotion efficiency to YouTube videos) (e.g. the followee has a lot of young female followers) • Advertising (Advertising media selection for our work) (e.g. anchor text generation (i.e., optimizing video description for promotion), advertising slot bid (i.e., followeereshare time selection))

  20. Other user-bridged cross network application Challenge Data hard to get! Taobao Topic Tweet Topic 1 user recommend Advertisement Video 2

More Related