ON THE INFLUENCE PROPAGATION OF WEB VIDEOS

ABIDHA.V.PROLL NO:01MCSSE 10808/12/2014

OBJECTIVESINTRODUCTIONRELATED WORKEXISTING SYSTEMPROPOSED SYSTEMTECHNICAL CONTRIBUTIONSADVANTAGESAPPLICATIONSCONCLUSIONREFERENCES

INTRODUCTION

Web videos nowadays influence society like never before in history.

It is utterly important to identify how the propagation took place,

i.e., to determine if a popular video on a video sharing website actually originated from that website, or it is merely a projection of influence from somewhere else of the cyberspace

Cont…

The problem to solve is extremely important to the video sharing site owner

video-sharing sites have developed schemes to encourage users to upload content.

Advertisers often exercise multiple partnership models for online videos.

Cyber cell sometimes need to know the origin of some video

RELATED WORK

Fist we survey the recent studies in event discovery and other mining tasks in social networks

Twitter - sensor network for event detection ,with semantic analysis of tweets establishes a probabilistic model which derives the probability of an emerging event from the occurrence reading of related tweets.

Bayesian filters, it determines the location of the event. It is reported effective for earthquakes, typhoons etc

A statistical model PET is used to track events in social networks.

Cont… A model called Social Pixel is proposed for aggregating social interests and detecting emerging events.

A framework Social Transfer is to analyze trending topics on the Internet for socialized video recommendation uses similar cross- domain analysis to predict the popularity .

Later leverages both Page Rank and video near-duplicate detection techniques for the same task.

A novel technique by jointly considering local regression and global aligned when learning for a metric that could handle cross media retrieval with multi-modalities.

EXISTING SYSTEM

Understanding Video Interactions using rank

weighted Laplacian matrix (affinity matrix)

Anti-social behavior such as self-promotion and other types of content pollution identification

The model is closely related to manifold learning and local learning

PROPOSED SYSTEM

The proposed method inspired by previous but there are two key differences.

Previous , which can not be applied to out-of-sample data.

proposed method is not only inductive, but also very efficient .

it is possible to use it to handle large-scale data.our method simultaneously reduces the noise in the

data by dimension reduction , which is not considered

in previous.

TECHNICAL CONTRIBUTIONS

To model an online video’s propagation and influence in the cross-community cyberspace, A Unified Virtual Community Space (UVCS)that captures the propagation history of an online video.

We propose an advanced learning method called Noise-reductive Local-and-Global Learning (NLGL)

UNIFIED VIRTUAL COMMUNITY SPACE (UVCS)which contains the web pages relevant to the video.

Each page has a time stamp, indicating the publishing time

or modification time of the page

Each page receives a set of inbound links , the link

relations among pages inside the UVCS

Each page’s rank in the UVCS is known.

Complementary information collected from other sources.

UVCS FEATURE REPRESENTATION

x = [ nt, tr, r, ir, ni, ipr, nip, L1 ◦W1,L2 ◦W2,NT,NB,NN, wi ].

All the related pages are published with nt and tr.

[r, ir, ni, ipr, nip,NT,NB,NN, wi] The second part contains

the search engine’s rank of the video page as well as

the related references in various communities of the UVCS.

[L1 ◦W1, L2 ◦W2] The rest of the feature vector helps

to evaluate both the propagation and the influence of the

online video.

UVCS FEATURE REPRESENTATION TABLE

NLGL LEARNING OBJECTIVES:

– THE METHOD SHOULD BE ABLE TO REDUCE NOISE.

The UVCS feature is a combination of multiple semantic components, the significance of each component is not specified

in the raw feature .Fields of the UVCS feature may be missing for some feature vectors due to the diversified nature of web pages.

The method should be able to learn a classification/ranking model with a relatively limited number of learning data, and then apply the obtained model on out-of-learning-set data. Consequently, it should be able to preserve the structure of the learning set.

Noise-reductive Local-and-Global Learning

THE NLGL ALGORITHMUtilizing The Un-annotated Sample Data

Obtain a training dataset with n total videos and n‘ manually annotated videos (n’< n), as X = [x1, . . . , xn] ^T R ^n×d, ∈

// where xi is the d-dimensional UVCS feature vector for the i th video in the dataset.

X^l = [x1, . . . , xn ]^T. We use Y^l = [y1, . . . , yn ]^T R^n×c to ∈ denote the class lables or ranks for the manually annotated videos X^l, // where c is an integer greater or equal to one. Note that Y^l is a unified presentation of the class labels for the propagation and the ranks for the influence ranking.

// c is the number of classes, and the label yi for the ith labeled video in X^l

• where Wo R^d×c is the classifier which is learnt from ∈ manually annotated data and is able to predict the labels for the un-annotated data.

• To leverage the unlabeled data for a better performance, it is desirable to design an algorithm which is able to predict the labels of the unlabeled videos by exploiting the data distribution of X.

Cont…

………….(1)

• To predict the label of new videos, a straightforward method is to minimize the empirical error for the following regression model:

CLASSES OF VIDEO PROPAGATION PATTERNS

Preserving Local Structure for the Annotated Data

o To predict the ranking score of each datum and then all the local linear regression models are optimized globally.o Predicted label of all the training data , sum up the loss functions for each xi and minimize the total loss.

o Denoting the column vector with all ones, we derive the objective as:

equation is written as:Despite the high accuracyIt is not able to predict the labels of the videos outside the training set.

………….(2)

Dimension Reduction and the Complete Model

o If linear transformation, which transforms the video data X in feature space to a more compact and accurate representation Z R^n×d∈ // where d’ ≤ d is the reduced dimensionality. o The transformation between X and Z can be formulated as:

Z = XQ

// where Q R^d’×d ∈ is the transformation matrix.

o If Q is properly trained, the classification based on Z would result in better performanceo we integrate training data label prediction, dimension reduction and classifier inference into a joint framework and propose to minimize the final objective function of NLGL:

………….(3)

where λ1 and λ2 are parameters.

o The first term aims at learning an optimal dimension reduction transformation and a classifier simultaneously.

o The second and third terms predict the labels of the unlabeled training data.

o Besides, ||XQW − F||F ^2 can be regarded as a regularizer of the local learning algorithm, which considers the global information to the predicted labels F.

………….(4)

Iterative Optimization

1. Fix F and optimize Q and W. By setting the derivatives of Equation (4) with respect to W to be 0,

Substituting W in Equation (4) by Equation (5), we arrive at:

2. Q and W are fix to optimize F. By setting the derivativesin Equation (14)

The two steps are iteratively performed until convergence.

………….(5)

………….(6)

………….(7)

ADVANTAGES

With this model we are able to predict the propagation

pattern and influence ranking for any new online video.

Extensive experiments are conducted on popular online videos to verify the effectiveness of the proposed framework.

Some observations on propagation and owners’ behaviors are also provided.

APPLICATIONS

In future research, there is great potential for us to

extend this research and enable it with other interesting

capabilities. The most likely problem would be the identification

of the actual origin of the video. Also investigating

possibilities of establishing inter-sharing-site influence

model to analyze how video sharing site influence each

other.

CONCLUSION

Determine how it got popular through it’s analyzing life cycle. Also give a rough estimation of its general influence on the Internet.

First we have made pioneer effort to model a video’s propagation and influence in cyberspace by a unified presentation, namely UVCS.

NLGL exploits the benefits of local learning, manifold structure and dimension reduction .

Successfully model the propagation classification and influence estimation tasks into a classification problem and a ranking problem under the same learning framework.

REFERENCES

1. J. Liu et al., “Presenting diverse location views with real-time near-duplicate photo elimination,” in Proc. IEEE 29th ICDE, Brisbane, QLD, Australia, 2013, pp. 505–516.2. S. D. Roy, T. Mei, W . Zeng , and S. Li, “Towards cross domain learning for social video popularity prediction,” IEEE Trans. Multimedia, vol. 15, no. 6, pp. 1255–1267, Oct. 2013.3.S. D. Roy, T. Mei, W . Zeng , and S. Li, “ Social transfer : Cross domain transfer learning from social streams for media applications,” in Proc. ACM Multimedia, New York, NY, USA, 2012, pp. 649–658.4. Z. Wang et al. “Propagation-based social-aware replication for social video contents,” in Proc. ACM Multimedia, New York, NY, USA, 2012, pp. 29–38.

QUESTIONS