The development and popularization of information technology and Internet had a profound impact on people's social interaction model, and also brought a great change in computing technology. The research of information science and technology pays attention to specific technical issues in the past , but now takes advantage of computing methods to simulate and analyze social theory and social issues on a higher level, which is a cross research between computing science and social science . In recent years, with the rapid rise and development, social computing receives great attention from domestic or foreign computer science and related interdisciplinary areas. At the same time, in order to effectively deal with new social problems and challenges caused by complex and dynamic changes in the network, we need to develop the basic theory, modeling, analysis and calculation methods of social computing urgently.
Research on Some Key Problems of Social Computing
Social computing is a research area in which social behavior and computing systems are crossed in recent years. It uses modern computer technology to make depth social data extraction on the virtual network, and completes the dynamic modeling and experimental simulation, which can accurately understands the dynamic characteristics of virtual society and the operation of the law for the virtual network of social management and government decision-making reference.This subject is based on the major national demand-oriented, comprehensively utilizes the forefront of research results of computational science, information science, system science, social sciences and other disciplines, and focuses on a number of key technical issues of the social computing under the new network environment. These problems include massive data information processing, social system modeling, social network structure analysis, social group behavior analysis, and the application to news communication, public safety, business management and many other social sciences. At last, we establish a unified social computing platform to enhance our school in the field of social computing influence, and promote the integration of science and engineering and humanities and social disciplines.
A fundamental assumption of Web user behavioral modeling is that the user’s behavior is consistent with the Markov process，and the user’s next behavior only depends on his current behavior regardless of the historical behaviors of the past. However，Web user’s behavior is a complex process and often driven by human interests．We know little about regular pattern of human-interest. In this paper，we explore the use of block entropy as a dynamics classifier for human-interest behaviors. We synthesize several entropy-based approaches to apply information theoretic measures of randomness and memory to the stochastic and deterministic processes of human-interests by using discrete derivatives and integrals of the entropy growth curve. Our results are，however preliminary，that the Web user’s behavior is not a Markov process，but a aperiodic infinitary long-range memory power-law process. Further analysis finds that the predictability gain can exceed 95.3 percent when users click 7 consecutive points online. In the era of big data，this can provide theoretical guidance for accurate prediction of online user’s interests.
If we see the Web as a virtual living organism，according to the metabolic theory， the websites must absorb "energy" to grow，reproduce and develop. We are interested in the following two questions：（1）where does the "energy" come from? （2）will the websites generate macro influence on the whole Web based on the "energy"? We would consider the influence as metabolism and users’ attention flow as energy for the websites. We study how collective attention distributes and flows among different websites by the empirical attention flow network. We find that the macro influence of websites scales sub-linearly against the collective attention flow dwelling time， which is not consistent with the heuristics that the more users’ dwelling time is，the greater influence a website will have. Further analysis finds a supper-linear scaling relationship between the influence of websites and attention flow intensity. This is a websites version of Kleiber’s law. We further notice that the development cycle of the websites can be split into three phases：the uncertain growth phase，the partially accelerating growth phase，and the fully accelerating growth phase. We also find that compared with the widespread hyperlinks analysis models，the attention flow network is an effective theoretical tool to estimate and rank the websites.
If we see the Web as a virtual living organism，according to the metabolic theory，it must absorb "energy" to grow and evolve. We want to know：1）where does the "energy" of the Web come from? 2）what are the common patterns of this "energy" flow? We make a conjecture that the websites survival and development highly rely on the energy，which is online collective attention flow. Knowing the distribution and the common patterns of collective attention flow among different websites is important to understand the underlying dynamics of human online behaviors. We define a set of basic variables related to weights of each website，including the attention flow traffic intensity，the online collective dwelling time，and the dissipated attention flow，to infer the common patterns of co-evolution of the Web and online collective behaviors. A number of interesting common patterns are revealed by this complex system：1）The heterogeneity of basic variables can be characterized by a rank-ordered DGBG（discrete version of a generalized beta distribution）curve；2）The allometric scaling laws and the dissipation laws of the Web evolution with collective attention flow，which can be seen as the Web system version of Kleiber's law，which is a widely used ecological theory；3）The gravity law，a common law in economics，ecology，and even a natural phenomenon in the whole universe，has been found in the Web system；4）The Heaps' law, a common phenomenon in characterizing the natural language，has been found in the growth of distinct websites in online collective attention flow. These common patterns are very significant in attention flow network，and they will play a more important role in quantifying the Web evolution and online collective behaviors prediction.
- Li Yong，Zhang Jiang，Meng Xiaofeng，Wang Changqing. Quantifying the influence of websites based on online collective attention flow. Journal of Computer Science and Technology(JCST)，2015，30(6):1175-1187 (SCIE，EI：20154801631620)
- Li Yong，Meng Xiaofeng，Liu Ji，Wang Changqing. Study of the long-range evolution of online human-interest based on small data. Journal of Computer Research and Development，2015，52(4):779-788 (EI：20151900826680)
- Meng Xiaofeng，Li Yong，Zhu JH. Social computing in the era of big data：opportunities and challenges. Journal of Computer Research and Development，2013，50(12)：2483-2491 (EI：20140217179642)
- Yong LI, Xiaofeng MENG, Qiang ZHANG, Jiang ZHANG, Changqing WANG. Common patterns of online collective attention flow[J]. Science China(Information Sciences),2017,60(05):258-260.
- Lou X, Li Y, Gu W, et al. The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows[J]. PLOS ONE, 2016, 11(11).
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