Social network spam filtering method based on distributed matrix decomposition feature extraction

Abstract

The invention relates to the technical field of network spam filtering, in particular to a social network spam filtering method based on distributed matrix decomposition feature extraction. The social network spam filtering method based on distributed matrix decomposition feature extraction comprises the following steps that (1) a user and attribute matrix of a social network is established; (2) feature extraction is conducted on the user and attribute matrix of the social network based on distributed matrix decomposition; (3) potential feature vectors are classified, and whether the potential feature vectors are social network spam or not is judged. The method is beneficial to efficiently filtering spam data in the social network.

Description

technical field [0001] The invention relates to the technical field of network garbage filtering, in particular to a social network garbage filtering method based on distributed matrix decomposition feature extraction. Background technique [0002] At present, social networks have played an important role in people's lives and have an impact that cannot be underestimated on people's information acquisition, thinking and life. Through social networks, users can communicate through chat rooms, create personal homepages to share favorite information, maintain more direct contact with friends, create a large social circle and find lost friends, follow other people's homepages and share Wait. However, the data in social networks has the coexistence of true and false, massive, rapid dissemination, and unbounded quantity. Due to the coexistence and massiveness of true and false data, there are a lot of unreliable content in social networks, such as fraudulent advertisements, hate...

AI Technical Summary

Problems solved by technology

However, the data in the social network has the coexistence of true and false, massive, rapid dissemination, and unbounded quantity, etc.

Due to the coexistence and massiveness of true and false data, there are a lot of unreliable content in social networks, such as fraudulent advertisements, hate speech, fake news, etc. These contents lack practical value, but may be of great significance to subsequent social data mining, User behavior analysis and resource recommendation accuracy have a negative impact. In this context, spam filtering in social networks has become an urgent problem to be solved.

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