Abstract
With the rapid rise of microblogging, social networking sites, and mobile Internet, users access to information, public participation, and expression of their demands is increasing significantly. Thus, the challenges faced by public opinion monitoring and guidance are becoming increasingly serious. To address the problems of mobile social network public opinion monitoring, this paper presents an intelligent situation awareness analysis. Intelligent situation awareness combines with the timing of memory hierarchy, extracting information flow characteristics better suited to a highly uncertain, scale-free dynamic information environment, effectively reducing the interference of false alarm history data; is based on robust self-learning screening methods to achieve automatic screening of the node data extraction features and to facilitate the automatic aggregation of situation awareness data in mobile social networking application server environments and unattended scenarios; builds situation knowledge to improve robustness and not lose the characteristics or cause false alarms because of dynamic changes in the network topology of small-world, making situation awareness more suitable for small-world network environments; makes empty inspection matrix scouring the results and eliminating false match points to accelerate visualization. With the maturing of the mobile social networking environment and doubling of the number of users, intelligent situation awareness provides protection to further enhance the level of intelligence of the emergency information platform and facilitate effective monitoring public opinion.
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