The invention provides a false news prediction method based on a relational network and an attention mechanism. The method comprises the following steps: firstly, obtaining a text feature vector and arelational network feature vector of news; fusing the text feature vector and the relational network feature vector through a two-layer neural network to obtain a news prediction function; and finally, optimizing the classification prediction model in combination with a news prediction function to obtain a final prediction classification function of news. According to the method, on the basis ofintroducing multi-source and multi-type features into prediction of false news, a relationship network feature vector is obtained by obtaining a news edge information relationship network structure, and the relationship network feature vector is fused with a news text feature vector to obtain a news prediction function; an attention mechanism is introduced into the acquisition of the relationshipnetwork feature vector, and a more accurate relationship network feature vector is acquired through attention weight; a better news prediction effect is obtained by optimizing a plurality of tasks ina multi-task training mode.

False news prediction method based on relational network and attention mechanism

Embodiments of the invention provide a news category detection method and system. The method comprises the steps of obtaining a content text and a social attribute text of to-be-detected news; inputting the content text and the social attribute text into a news category coarse detection model to obtain a first category text of the to-be-detected news output by the news category coarse detection model; and inputting the first category text, the content text and the social attribute text into a news category fine detection model to obtain a second category text of the to-be-detected news outputby the news category fine detection model. Through the news category coarse detection model and the news category fine detection model, feature extraction and feature fusion are realized, and social attribute texts are considered during feature fusion, so that a fusion result is more accurate, and a detection result determined based on the fusion result is more accurate.

News category detection method and system

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News category detection method and system