A social network rumor identification method based on feature aggregation

Abstract

The invention discloses a social network rumor identification method based on feature aggregation, and the method comprises the steps: designing time sequence propagation mode features acceptable by adeep neural network and time sequence text features, constructing a rumor detection model by using a feature aggregation technology, and carrying out the final detection and early detection of a rumor. The problem that propagation mode characteristics of social network event propagation are difficult to serve as machine learning model input is solved, the propagation mode characteristics do not depend on characteristic engineering and field knowledge, the influence of various factors in the actual propagation process is comprehensively embodied, and the method can be effectively applied to different rumor identification scenes; The defect that the quality of feature data is reduced due to huge difference of the number of messages contained in different samples is avoided, the problem thata single model is difficult to deal with heterogeneous information in a traditional machine learning method is solved, and compared with an existing rumor identification method, the accuracy is obviously improved.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and in particular relates to a social network rumor information feature modeling and detection method. Background technique [0002] With the development of social networks, the amount of information has increased dramatically. However, the quality of information cannot be guaranteed, and false information represented by rumor information has penetrated into almost every corner of the social network. Therefore, how to realize automatic information credibility evaluation and predict the authenticity of social media information has high practical significance. [0003] The identification of unknown rumors is one of the urgent requirements for information credibility assessment and information content security. Social psychology defines rumors as news whose authenticity has not been confirmed or intentionally falsely declared. The spread of rumors is harmful to people's lives and social stab...

AI Technical Summary

Problems solved by technology

Feature extraction is a process of obtaining sample distribution characteristics by using statistics and other methods. There are differences in sensitive feature types in different scenarios. Therefore, traditional machine learning methods rely heavily on feature engineering and are not suitable for complex and changeable social networks.

Secondly, the feature extraction process of traditional methods also requires rich domain knowledge, including the attributes of rumors, the current network environment structure, etc., but due to human interference, it will inevitably cause deviations.

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