Research On Information Dissemination And Topics Growth Trends Prediction In Social Networks

Driven by the fast development of the Web2.0and information network technology, huge numbers of individuals who have been attracted by various popular applications, are crowding into the social networks. As a self-media, users in social networks can participate in the interactions with other individuals anytime, anywhere and by utilizing any access methods. This new, flexible, fast interactive mode can greatly shorten the evolution time in which public opinion is generated, fermented, and disseminated. At the same time, participants have the highly dynamic, self-organization, and heterogeneity characteristics, and social opinion may influence network opinion with the nonlinear mapping relationship. All of above negative factors make the dissemination and evolution process of network opinion becoming more randomly and complicatedly. However, the traditional models and research methods of public opinion are difficult to accurately describe not only the microscopic interaction behavior between users but also the macroscopic phenomenon of dissemination and evolution. In view of this, we use the interdisciplinary ideas and methods to study information dissemination mode in social networks, influential users mining, topic detection and trends forecasting issues, trying to find and restore the information dissemination in social networks, to establish mathematical models which can characterize these laws, and to find relevant strategies which can promote or inhibit information dissemination. Our work may help to understand the evolution process of public opinion in social networks, to understand the complex group behavior deeply, and also provide some of exploratory theoretical results for the study of complex systems.The work of the dissertation is supported by the National Natural Science Foundation of China (No.61172072,61271308), Beijing Natural Science Foundation (No.4112045), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20100009110002). Main contributions of the dissertation are as follows:1. We propose an epidemic model for information dissemination in social networks based on the theory of tie-strength. The model considers infectious probability as a function of the ties strength, and then establishes the dynamic evolution equations which describe the evolution process of different types of nodes. Moreover, we investigate numerically the behavior of the model on a real scale-free social site with the exponent γ=2.2. We verify that the strength of ties plays a critical role in the rumor diffusion process. Specially, selecting weak ties preferentially cannot make rumor spread faster and wider, but the efficiency of diffusion will be greatly affected after removing them. Another significant finding is that the maximum number of spreaders is very sensitive to the immune probability P and the decay probability v. We show that a smaller μ or v leads to a larger spreading of the rumor, and their relationships can be described as the function ln(max(S))=Av+B, in which the intercept B and the slope A can be fitted perfectly as power-law functions of μ. Our findings may offer some useful insights, helping guide the application in practice and reduce the damage brought by the rumor.2. We establish an information dissemination model based on social memory and tie-strength. The model considers that the social reinforcement factor (social participation of public opinion), the memory effect factor (the number of contacting information for individuals) as well as interpersonal relationship (strength of dissemination ties) will affect the decision-making process of the individual, and then affects the information coverage. Some simulations on two real social sites can prove the following conclusions. Firstly, network topology characteristics will affect the strength and coverage of the information dissemination. Secondly, the higher degree of public opinion participation can lead to greater chance for information spreading in the network. Thirdly, the immune node plays the role of information firewall in the network with higher average degree and clustering coefficient. Finally, the average number of contacting information for individuals will decrease with the increasing participation of the publis opinion. The model can restore the basic characteristics of the information exchange in the social networks, and provide a theoretical basis for further study.3. We study the nodes centricity characteristics and identify influential nodes for spreading dynamics. First, we analyze the distribution of four kinds of centrality indicators and their correlations in two typical social sites. Then, we propose a new centricity indicator based on the theory of ties strength, which is named as local weight indicator (LW). The indicator emphasizes the node influence is jointly determined by the quantity and quality of neighbors, and the complexity of LW is far lower than the closeness and betweenness. Moreover, LW has a finer-grained distinction than k-core indicator. We use SIR model to evaluate the performance of LW indicator in two real social sites. Simulations show that our method can well identify the influencial nodes. The method can provide theoretical support for some applications.4. We study the method of hot topic detection and the algorithm of topics’growth trends prediction in social networks. First we design a lightweight system to detecte hot topics, and then put forward a prediction method based on the BPNN. The results of empirical tests show that our approach is more effective than existing method ARIMA in the aspect of forecasting growth trends. Moreover, we apply wavelet denoising method, BPNN network structure optimization and adaptive learning rate adjustment strategies to improve the above model. Results show that the predicting performance of the optimized model has been improved significantly. Finally, we establish of a self-adaptive forecasting model based on the BPNN and ARIMA in social networks, which can expand the scope of its applications. These methods may help people to master the growth trends through public monitoring and early warning.