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College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology.

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College students face risks from online information and mobile social networks. This study analyzes complaint leakage to enhance awareness and protect students from fraud and security threats.

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Area of Science:

  • Computer Science
  • Information Security
  • Social Science

Background:

  • Advancements in big data technology enable rapid information access for college students.
  • College students frequently use mobile social networks for information and communication.
  • Online platforms present risks of inadvertent personal advice purchases and information leakage.

Purpose of the Study:

  • To segment college students on mobile social networks using natural clustering algorithms.
  • To construct an information leakage and risk assessment model for college students.
  • To analyze psychological components and communication channels of complaint leakage on microblog platforms.

Main Methods:

  • Application of natural clustering algorithms for student segmentation.
  • Risk assessment design for information leakage.
  • Psychological analysis of complaint leakage using microblog data and user complaints.
  • Social prospect method and surrogate analysis for data collection and dimension identification.

Main Results:

  • Identification of four distinct user groups: Dining, Social, Teaching, and Large users.
  • Description of user profile features and importance across eight categories.
  • Analysis of key components and communication channels in college students' complaint leakage.

Conclusions:

  • Enhancing college students' awareness of social network and mobile information security is crucial.
  • Standardizing online advertisements and improving security measures can protect students from fraud and malicious plans.
  • Proactive measures are needed to address evolving network security challenges for mobile assets.