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## Dynamic public opinion evolvement modeling and supervision in social networks

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With the booming of social networks, a large proportion of public opinion is expressed and transferred through social networks. With complex structure and varied evolving patterns, monitoring public opinion in social networks is not well solved for a long period. Motivated by the purpose of public opinion and social network evolvement rules, we developed a public opinion dynamic evolvement model and supervision mechanism in social networks. We assume our research target is a topic-based and opinion-driven social network that is the most popular one in studying public opinion. The background network of our model is a temporary social connection that we name as tornado-type social network (TTSN). In a TTSN, public opinion evolvement is decided by two basic factors: sentiment activity (SA) and opinion consistency (OC). Based on the observation of SA and OC, we have designed a model to supervise and optimize the public opinion express in social networks. Under the model, the public opinion supervision is regressed to an optimization problem. By solving the problem, both our deduction and simulation results show that public opinion in a social network tends to evolve from chaos to consistency, and SA follows approximately ideal normal distributions before a time limit T.

Chapter Contents:

• 11.1 Introduction
• 11.1.1 No combined analysis model for public opinion in social networks
• 11.1.2 Dynamic challenge for public opinion sampling and inspection
• 11.1.3 Time efficiency for public opinion supervision
• 11.2 Related works
• 11.2.1 Evolvement based on topo discovery and graph structure change
• 11.2.2 Evolvement based on influence analysis
• 11.2.3 Evolvement based on sentiment and NLP
• 11.3 Dynamic public opinion evolvement modeling
• 11.3.1 System model and definitions
• 11.3.2 Conditional optimization problems in dynamic public opinion evolvement
• 11.4 Evolvement of public opinion and supervision
• 11.4.1 Preparation algorithms
• 11.4.2 Evolvement and supervision algorithms
• 11.5 Data and results
• 11.5.1 Datasets and environment
• 11.5.2 Parameters (μ,λ, (t))
• 11.5.3 OC–SA optimization performance in TTSN evolvement effectiveness
• 11.5.3.1 Time delay/time costs
• 11.6 Discussions
• Acknowledgments
• References

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