Research On Analysis And Prediction Of Popularity Evolution In Online Social Networks

Online social networks(OSNs)reach into every aspect of modern life.OSNs have played a significant role in the way we live.Because of the unprecedented amount of information load in OSNs,the scarce and valuable resource is now human's attention.Therefore,there has been a great deal of research interest in human's attention,known as the study of popularity analysis and prediction.This is one of the most fundamental research areas of OSNs.The analysis and prediction of OSN popularity not only help us better understand user behaviors and social phenomena,but also provide insights on application designs and public opinion guidance.Nowadays,popularity analysis and prediction have been greatly studied.Most of existing work,however,has overlooked the fact that popularity evolves over time during the process of information diffusion.Popularity evolution has remained largely unexplored.Therefore,this thesis aims to study the analysis and prediction of popularity evolution in OSNs,which is not trivial because of two challenges:highly dynamic popularity evolution,and numerous factors having influences on popularity evolution.To take on the challenges,this thesis addresses three aspects of popularity evolution on the Tianya topic dataset and the Twitter Hashtag dataset:pattern analysis,factor analysis,and evolution prediction.The contribution of this work is mainly as follows.(1)In order to provide insights on what existing work is missing,we propose a method for modeling evolution pattern based on time series analysis,which provides insights on understanding popularity evolution pattern.A time series method is conducted for analyzing three evolution patterns,namely,Average,Trend,and Cyclicity.Part 1 uses the three evolution patterns to construct both a fitting model and a predicting model for popularity evolution.Experiments show that our models outperform baseline models.(2)In order to address the fact ignored by existing work that popularity undergoes key events(burst,peak,fade)over its evolution,we propose a method for understanding factors of different stages of popularity evolution based on correlation analysis,which sheds light on how factors affect popularity evolution.The following findings are drawn through correlation analysis.A high correlation is found between key event times and levelTime,which means that levelTime is a dominant factor for popularity evolution.Furthermore,the number of celebrities has a strong correlation with key events during the early stage of popularity evolution.(3)This thesis proposes two new prediction tasks for popularity evolution and provides prediction solutions based on machine learning and deep learning.For predicting when popularity bursts,peaks,and fades,Part 3 conducts feature selection to remove irrelevant and redundant features that are used in Part 2.The remaining features are used to train an SVR model.For predicting active periods,a DNN-based(deep neural-network-based)prediction framework is presented.Part 3 presents how to vectorize factors used in Part 2 into two types of embeddings:the LSTM-based(long-short-term-memory-based)dynamic embedding and CNN-based(convolutional-neural-network-based)static embedding.The dynamic embedding and static embedding are together fed into a predictor using a fully connected neural network.Experimental and comparative results show the superiority of our prediction solutions.(4)We develop an application platform for popularity evolution of online viral events.From social media websites(Toutiao,Tencent News,Zhihu,etc)we collect different types of data of viral events,including text data,picture data,and video data.Next,3 layers are implemented in a bottom-up fashion:the data layer,the storage layer,and the application layer.Popularity evolution visualization and prediction are presented in the application layer.