Research On Key Technologies Of Public Opinion Evolution In Online Social Networks

In the last decade, the coming-together of technological networks and socialnetworks boosts social interactions through online spaces, which enableinformation exchange and online interactions autonomously with no centralcontrol in a multi-user environment, and exert non-negligible influence on publicopinions. With the increasing importance of network public opinions inunderstanding consensus formation and evolution through online socialnetworks, many efforts have focused recently on evolution analysis of networkpublic opinions. However, due to the unique complexity, the mechanism ofnetwork public opinion evolution on online social networks is far from clearlydefined, although many efforts are contributed. Analysis and modeling ofnetwork public opinion evolution is still an important open problem despite theattentions it has attracted recently.(1) We study concepts and methods of evolution analysis for public opinionsin online social networks. Concepts and processes of the formation of networkpublic opinions are defined firstly. We argue that network public opinionevolution evolves information, social structure and member behaviors, whichdeserves complete and comprehensive study as the foundation of opinionevolution on online social networks. Key techniques implementing evolutionanalysis of network public opinions are further illustrated within the framework.(2) We investigate methods of topic evolution analysis as a part of evolutionof network public opinion. Based on the hierarchy of topics consisting ofcorrelated sub-topics, a framework is proposed including detection andcorrelation analysis of sub-topics. The latent semantics of textual data ismodeled using Latent Dirichlet Allocation (LDA). With consideration of timeinformation, text streams are partitioned into slices, and topic evolution model isproposed, where history topic models provide prior knowledge for topic detectionin the current time-slice. Furthermore, a topic evolution algorithm based on LDAis presented with Bayesian model selection for the appropriate topic numbersand parameters estimation via Gibbs sampling. Furthermore, we define types ofcorrelations of sub-topics, and a method based on relative entropy is proposedto organize correlated sub-topics. We experimentally verify that the method iseffective and efficient for detecting topic evolution of network news and BBSposts.(3) We empirically study the structure and evolution of online socialnetworks using a large scale data set from a forum of BBS. Among various types of online social networks, Bulletin Board System (BBS) is one of the mostpopular to allow people sharing common interests to discuss thoughts or ideason topics. By modeling members of the BBS forum as nodes and theirinteractions as links, we treat the BBS network as a directed graph withconsideration of the closeness of interactions and uncover characteristics of theBBS network, which are fundamental to opinion dynamics on online socialnetworks.More specifically, the mechanisms of growth of nodes and links areinvestigated, indicating mechanisms quite different from existing models.Another important observation is the bilateral scale-free power-law distributionsof in-degree and out-degree, which exhibit significant positive correlation. Thenetwork, on the other hand, shows the “small world phenomenon” with highweighted clustering coefficient and small average shortest path. Furtheranalyses on the dependencies of average strength of nodes as well as averageweighted clustering coefficient on degree confirm the correlations betweenweighted properties and the network topology. The hierarchy of members isproposed, indicating the heterogeneity of member influence. The quantitativeanalysis of member behavior presents power-law distribution of interevent timebetween two consecutive postings in BBS.(4) We discuss opinion dynamics on the BBS network by taking account ofboth social influence and self-affirmation, which addresses state transitions ofactors at the microscopic level, and leads to rich dynamic behaviors at themacroscopic level.Both social influence and individual diversity play important roles in opiniondynamics. Social influence is decisive for individual adoption, which is recentlyverified in online social networks. The more neighbors take an opinion, the morepossibly an actor is convinced to adopt it. On the other hand, online socialnetworks consist of actors of different psychological types and social interactions,which exhibit heterogeneous self-affirmation. At each time step, an actor ischosen to update its opinion according to the interplay of social influence and itspersistence in its current opinion, where each actor is assigned a weightproportional to the power of its strength for its persistence.We investigate the configurations of reaching the final consensuses, andfind that the advantage of weighted fraction, instead of the population, of oneopinion over the other one leads to the consensus. Given a set of typically initialfractions of opinion+1and opinion-1, the consensus converges towards opinion+1and-1, respectively, when the highest-strength or the lowest-strength actorshold opinion+1. Starting from totally random initial distributions, the opinionleading to the consensus features an advantage of the initially weighted fraction over the other, which also holds in the case of equally random distributions oftwo opinions. That is, whether an opinion denominates depends on the initiallyweighted fraction of it. This indicates that high-strength actors play an essentialrole in opinion formation with strong social influence as well as high persistence.Further investigations show that individual diversity slows down the orderingprocess of consensus. Our study provides deep insights into the role of socialinfluence and individual diversity on opinion formation in online social networks.Comparison study shows that opinion evolutions on heterogeneousnetworks and homogeneous networks show dramatic differences. Morespecifically, opinion evolutions on BBS network and scale-free network indicatesimilar characteristics as heterogeneous networks. In contrast, evolutions onsmall-world network and random network are different from those on the aboveheterogeneous networks. Due to the heterogeneity, opinion distributions on BBSnetwork and scale-free network matter.