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Knowledge graph guided false news detection method

A technology of knowledge graphs and detection methods, applied in neural learning methods, unstructured text data retrieval, instruments, etc., can solve problems such as lack of external prior knowledge

Active Publication Date: 2020-04-24
WUHAN UNIV
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Problems solved by technology

[0004] When the inventors were studying fake news detection, they found that the existing methods were limited to extracting features from the news text itself, and lacked the introduction of external prior knowledge.

Method used

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  • Knowledge graph guided false news detection method
  • Knowledge graph guided false news detection method

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Embodiment Construction

[0083] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0084] The present invention aims at the disadvantage that the existing fake news detection method cannot introduce additional prior knowledge, by introducing the knowledge graph as the prior knowledge, combined with the deep learning model, and using the knowledge graph to guide the deep learning model to detect fake news.

[0085] Such as figure 1 As shown, the fake news detection method guided by the knowledge map of the embodiment of the present invention includes the following steps:

[0086] First, obtain the data set of fake news detection task, use the named object recognition model and en...

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Abstract

The invention discloses a knowledge graph guided false news detection method which comprises the following steps: step 1, based on a false news detection data set, constructing a knowledge graph as background knowledge of false news detection, and pre-training a knowledge graph embedding model; step 2, extracting entities in a news text to be detected, and connecting the entities to the knowledgegraph; step 3, based on the news text and the entity, obtaining word level enhanced representation of the news text, and extracting news text word level features based on an attention mechanism; step4, obtaining news text word level representation, and extracting news text word level features based on an attention mechanism; step 5, based on the entity attention model, extracting entity featuresin the news text; and step 6, fusing the word-level features, the character-level features and the entity features of the news text to be detected, and performing authenticity detection on the news text to be detected. According to the method, the knowledge graph is introduced to guide the deep learning model to perform false news detection, so that the model recognition accuracy and generalization performance are improved.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to a method for detecting fake news guided by knowledge graphs. Background technique [0002] Fake news is news that is intentionally and truly false and may mislead readers. In recent years, with the development of Internet technology and social networks, the Internet has become the main source for people to obtain news information, and the resulting fake news has also become one of the hottest social and political topics in recent years. Special attention has been paid to the post-Brexit. Fake news has the characteristics of low cost, easy access and rapid dissemination, which can easily mislead public opinion, disturb social order and damage the credibility of social media. Therefore, it is necessary to study fake news detection and establish a scientific, reasonable, effective, efficient and accurate fake news detection distribution to effectively detect fake ne...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F16/35G06F16/36G06F40/205G06N3/04G06N3/08
CPCG06F16/3335G06F16/35G06F16/367G06N3/08G06N3/044G06N3/045
Inventor 刘金硕李晨曦邓娟李扬眉
Owner WUHAN UNIV
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