Mining Cross-network Association for YouTube Video Promotion Ming Yan, Jitao Sang, Changsheng Xu*. 1 Institute of Automation, Chinese Academy of Sciences, Beijing, China-Singapore Institute of Digital Media, Singapore, {ming.yan, jtsang, Large quantities of videos are consumed in YouTube and the trend is growing year by year. YouTube exhibits limited propagation efficiency and many videos remain unknown to the wide public due to the limited internal mechanism. External referrers such as social media websites arise to be important sources to lead users to YouTube videos, among which Twitter has grown to be the top referrer recently. The followee-follower and user-centric architecture has distinguished Twitter with significant information propagation efficiency. Motivation: For specific YouTube video, to identify proper Twitter followees with goal to maximize video dissemination to the followers. Introduction For our video promotion application, we claim that only large exposure to more audiences is not enough, what video promotion cares is the number of “effective” audiences, who are likely to show interest to the target video and with higher probability to take subsequent consuming actions. Therefore, the core of video promotion should both match with the interest of the Twitter followers and be cost-effective. Figure 1. Problem Illustration. Two Challenges The heterogeneous knowledge association between YouTube video and Twitter followee; How to define the “properness” of candidate Twitter followee for a specific YouTube video. To address the challenges one by one, we propose a three-stage framework as our solution: Heterogeneous Topic Modeling: To discover the latent structure within YouTube video and Twitter user spaces, respectively; Cross-network Topic Association: To address the discrepancy issue between the heterogeneous YouTube video and Twitter user spaces by mining cross-network topic association on a collective user-level. Referrer Identification:To define the “properness” of candidate Twitter followee for a specific YouTube video and match video to followee in a ranking-based method. Figure 2. The framework Heterogeneous Topic Modeling Goal To discover the latent structure within YouTube video and Twitter user spaces, and facilitate the subsequent analysis and applications in topic level. Through this stage, each YouTube video and Twitter user can be represented as distributions in the derived corresponding topic spaces. YouTube Video Topic Modeling The video topics are expected to span over both textual and visual spaces. We introduce a modification to the multi-modal topic model, Corr-LDA, as depicted in the figure 3. Figure 3. The graphical representation of inverse Corr-LDA Twitter Followee Topic Modeling Since the properness of Twitter followee is decided by the followers, we are interested in investigating into the followee-follower architecture in Twitter. Therefore, we represent each Twitter user (document) with all his/her followees (words) and apply the standard LDA on the user social graph for topic modeling. Advantage Don’t need an explicit association matrix Non-linear Non-overlapped users can be also utilized Discovered Topic Visualization Figure 5 and 6 show the visualization of some of the discovered topics in YouTube and Twitter, respectively. Figure 4. Perplexities for different topic numbers on YouTube and Twitter Figure 5. Visualization of discovered YouTube topics Figure 6. Visualization of discovered Twitter topics Cross-network Topic Association Innovation To address the discrepancy issue, we propose a solution that first aggregates YouTube video distribution to user level, and then exploit the overlapped users among different networks as bridge for association mining. Assumption if the same group of users heavily involve with topic A in network X and topic B in network Y, it is very likely that topic A and B are closely associated. YouTube User-Topic Distri. Aggregation Various Approaches for Association Mining via Overlapped Users Goal To enable topic distribution transfer between different networks, i.e., given user’s topical interest in YouTube videos, we can infer his/her most probably followed Twitter followee topics. 2. Regression-based Association To deal with the noisy user distribution issue, a regression-based optimization approach is proposed: When q = 1, this is a lasso problem and can be effectively solved by feature-sign search algorithm. When q = 2, this is a ridge regression problem and have an analytical solution. 3. Latent Attribute-based Association Topic Number Selection Assumption We assume that the latent structure is actually user attribute, i.e., it is the same user’s unique attribute values that give birth to his/her different activities and thus the cross-network topic distributions. A YouTube factor and a twitter factor are coupled to the same user attribute, and the same user should have identical coefficients when projected to the coupled user factors. Innovation Try to discover the shared latent structure behind YouTube and Twitter topic spaces on user level. Objective Function Evaluation for the final video promotion We utilize to evaluate our final video promotion problem. The result is shown in Figure 8. It demonstrates the advantages of our proposed framework to promote YouTube videos via Twitter network compared with other baselines. Figure 7. MAE for distribution transfer in Stage 2. Twitter Referrer Identification With the topical distribution transfer enabled, in this third stage, we first transfer the test video distribution to the Twitter topic space. Then we are devoted to matching the YouTube video with Twitter followee in the same Twitter topic space in a ranking svm- based scheme. Two critical issues should be addressed when utilizing the ranking-svm training scheme:  How to extract the video-followee pair features? We define the video-followee pair features as the vector product between the transferred video and Twitter followee distributions:  How to define the Ground-Truth (GT) properness for each video-followee pair in the training set? Both the follower interest and the cost to ask Twitter followee for help should be considered. Here, we treat the follower number of the twitter followee as the virtual cost, i.e., more popular a Twitter followee, more cost should be paid to ask him/her for help. To consider both of these, the Ground-Truth (GT) properness is defined as below: Evaluation of Transfer Error We utilize Mean Abosolute Error (MAE) on half of the overlapped users to evaluate the performance of topical distribution transfer between YouTube and Twitter. The result is shown in Figure 7. RandomPopularity Regression +Direct Regression +Weighted LA_all +Weighted LA_all +Direct Figure 8. for different settings