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Micro-blog spammer detection method based on fuzzy multi-class SVM

A detection method and user technology, applied in the field of social network security, can solve problems such as the impact of detection accuracy and the lack of consideration of multi-value classification

Active Publication Date: 2017-04-05
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most garbage user classification methods based on the SVM method basically only consider binary classification, and do not consider multi-valued classification
In addition, multi-class classifiers will generate mixed samples, which will have a certain impact on detection accuracy

Method used

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  • Micro-blog spammer detection method based on fuzzy multi-class SVM
  • Micro-blog spammer detection method based on fuzzy multi-class SVM
  • Micro-blog spammer detection method based on fuzzy multi-class SVM

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Embodiment Construction

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar meanings throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0034] figure 1 It is a schematic diagram of the overall process structure of the present invention. As shown in the figure, the present invention provides a microblog spam user detection method based on fuzzy multi-class SVM. First, analyze the behavior of Weibo spam users, divide spam users into three categories, and use the CDF curve to select features with high discrimination as training sample features; build a one-to-many SVM multi-class classifier to classify users; for multi-class The mixed samples generated by the classifier adopt the method of fuzzy processing ...

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Abstract

The invention discloses a micro-blog spammer detection method based on a fuzzy multi-class SVM. A one-versus-rest SVM multi-class classifier and a fuzzy mathematical theory are combined for micro-blog spammer detection. According to the conventional micro-blog spammer research, global spammer characteristics are analyzed, the analysis force is not enough, the analysis of the characteristics of certain kinds of spammers is not conducted, and the spammers escape detection of a detection system. According to the method, the characteristics of the micro-blog spammers is conducted, the spammers are divided into three categories, the one-versus-rest SVM multi-class classifier is constructed, fuzzy processing is performed on mixed classified samples generated by the multi-class classifier by employing a fuzzy clustering method, the combined classifier is obtained, and the detection precision is improved.

Description

technical field [0001] The invention relates to the field of social network security, relates to analyzing and processing spam users in social networks by using machine learning algorithms and fuzzy mathematics, and in particular relates to a social network detection method based on fuzzy multi-class SVM. Background technique [0002] Weibo, also known as MicroBlog, is a social platform for short and real-time information sharing. Users share short messages by following each other. Weibo, with its unique weak relationship chain system, discloses information sharing methods Won the welcome of the majority of netizens. In recent years, microblog platforms such as Sina Weibo and Twitter have become important information sharing platforms for Internet users. Here, people follow current news and share opinions and comments about facts and their own events. With the growth of Weibo users, the emergence of spam and spammers is inevitable; for example, worms from viral websites at...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06F17/30G06K9/62
CPCG06F16/951G06Q50/01G06F18/2411
Inventor 徐光侠高郭威宋洋洋刘宴兵常光辉齐锦蒋鹏李伟凤
Owner CHONGQING UNIV OF POSTS & TELECOMM
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