Popularity Prediction of Social Media Post Using Tensor Factorization

The traditional method of doing business has been disrupted by socialmedia. In order to develop the enterprise, it is essential to forecast the level ofinteraction that a new post would receive from social media users. It is possiblefor the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detectionstrategies that are user-based or population-based are unable to keep up with theseshifts, which leads to inaccurate forecasts. This work makes a prediction abouthow popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that istensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation bychoosing the top contents that were most like each other. The tensor is factorisedwith the application of the Adam optimization. It has been modified such that thebias is now included in the gradient function of A-PARAFAC, and the value ofthe bias is updated after each iteration. The prediction accuracy is improved by32.25% with this strategy compared to other state of the art methods.

Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

With the rapid development of networks,users are increasingly seeking richer and high-quality content experience,and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching.Therefore,it will be an effective solution to combine content popularity prediction based on machine learning(ML)and content caching to enable the network to predict and analyze popular content.However,the data sets which contain users’private data cause the risk of privacy leakage.In this paper,to address this challenge,we propose a privacy-preserving algorithm based on federated learning(FL)and long short-term memory(LSTM),which is referred to as FL-LSTM,to predict content popularity.Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection.Meanwhile,the caching policy in this paper raises about 14.3%of the content hit rate.

Video Popularity Prediction Based on Knowledge Graph and LSTM Network

The prediction of the popularity of online content,particularly videos,has recently gained significant attention as successful popularity prediction can assist many practical applications such as recommendation systems and proactive caching,as well as aid in optimizing advertising strategies or balancing network throughput.Despite much work being done on predicting the popularity of online videos,there are still challenges to be overcome:(1)popularity is greatly influenced by various external factors,resulting in significant fluctuations that are difficult to capture and track;(2)online video content and metadata information are typically diverse,sparse,and noisy,making the prediction task complex and unstable;(3)some data have temporal relevance,and the impact on popularity varies at different times.In this paper,we propose an Adaptive Temporal Knowledge Graph Network(ATKN)video popularity prediction model to address the issues surrounding video popularity prediction.First,we employ the attention-basedLong Short-Term Memory(ALSTM)network to capture the trend of popularity change.Then,we introduce anAttention-based FactorizationMachine(AFM)with attentionmechanism to model the feature cross of video content,thereby enhancing the distinction of importance after different feature crosses.Next,we use a RelationalGraph Convolutional Network(RGCN)to extract the associated features between entities in the knowledge graph.Finally,we propose a dynamic feature fusion method that adaptively assigns the weights of temporal features and content features at different time intervals by constructing an exponential decay function,thereby obtaining an effective and stable feature fusion module.Experimental results demonstrate the superiority and interpretability of ATKN on the MovieLens-20M dataset and the Microsoft Satori-built movie knowledge graph.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

A review of feature fusion-based media popularity prediction methods

With the popularization of social media,the way of information transmission has changed,and the prediction of information popularity based on social media platforms has attracted extensive attention.Feature fusion-based media popularity prediction methods focus on the multi-modal features of social media,which aim at exploring the key factors affecting media popularity.Meanwhile,the methods make up for the deficiency in feature utilization of traditional methods based on information propagation processes.In this paper,we review feature fusion-based media popularity prediction methods from the perspective of feature extraction and predictive model construction.Before that,we analyze the influencing factors of media popularity to provide intuitive understanding.We further argue about the advantages and disadvantages of existing methods and datasets to highlight the future directions.Finally,we discuss the applications of popularity prediction.To the best of our knowledge,this is the first survey reporting feature fusion-based media popularity prediction methods.

Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems.Time series,content and meta-feature are important features in news popularity prediction.However,there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance.This work proposes a novel deep learning model named Multiple Features Dynamic Fusion(MFDF)for news popularity prediction.For modeling time series,long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity.The typical convolution neural network gets headline semantic representation for modeling news headlines.In addition,a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement.A factorization machine is employed to model the interaction relationship between metafeatures.Considering the role of these features at different stages,the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically.During the training phase,thiswork designs a loss function based on Newton’s cooling law to train the model better.Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.

Research on Prediction Methods of Prevalence Perception under Information Exposure

With the rapid development of information technology,the explosive growth of data information has become a common challenge and opportunity.Social network services represented by WeChat,Weibo and Twitter,drive a large amount of information due to the continuous spread,evolution and emergence of users through these platforms.The dynamic modeling,analysis,and network information prediction,has very important research and application value,and plays a very important role in the discovery of popular events,personalized information recommendation,and early warning of bad information.For these reasons,this paper proposes an adaptive prediction algorithm for network information transmission.A popularity prediction algorithm is designed to control the transmission trend based on the gray Verhulst model to analyze the law of development and capture popular trends.Experimental simulations show that the proposed perceptual prediction model in this paper has a better fitting effect than the existing models.

StarIn:An Approach to Predict the Popularity of GitHub Repository

The popularity of repository in GitHub is an important indicator to evaluate its quality.Exploring the trend of popularity is a crucial guideline to study its development potential.Herein,StarIn,a stargazer-influence based approach is proposed to predict the popularity of GitHub repository.Using the followers in GitHub as a basic dataset,stargazer-following based network was established.The indicator,stargazer influence,was measured from three aspects of basic influence,network dynamic influence and network static influence.Experiments was conducted,and the correlation of StarIn was analyzed with the popularity of repository from the perspective of six characteristics.The experimental evaluation provides an interesting approach to predict the popularity of repositories in GitHub from a new perspective.StarIn achieves an excellent performance of predicting the popularity of repositories with a high accurate rate under two different classifiers.