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False news detection method based on knowledge perception attention network

A technology of attention and news, applied in the field of artificial intelligence, can solve problems such as insufficient understanding of news texts

Active Publication Date: 2021-02-05
NANKAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to solve the problem of insufficient understanding of news texts in the existing false news detection methods, and innovatively propose a false news detection method that integrates external knowledge

Method used

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  • False news detection method based on knowledge perception attention network
  • False news detection method based on knowledge perception attention network

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Embodiment Construction

[0068] The present invention proposes a false news detection method based on knowledge perception attention network, the main process of the method is as follows figure 1 shown. Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0069] The specific implementation process of the present invention is divided into six steps: obtain the news data set; extract the entity and entity context in the knowledge map as external knowledge according to the news text; encode the news text; encode the entity and the entity context; design attention Force mechanism, which assigns weights to entities and entity contexts; integrates news representation, entity representation and entity context representation, and classifies through a deep neural classification model. The following is a detailed description of the six-step implementation process:

[0070] 1. Data set acquisition;

[0071] After obtaining the news fr...

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Abstract

The invention discloses a false news detection method based on a knowledge perception attention network, and belongs to the technical field of artificial intelligence. The method comprises the steps that related knowledge in a knowledge graph is extracted based on news texts, the news texts and the extracted related knowledge serve as input data, a false news detection model based on knowledge perception is constructed, and news samples are classified. Firstly, entity mentions in news are recognized through entity links and aligned with corresponding entities in a knowledge graph, and an entity sequence is obtained; secondly, for each entity in the entity sequence, obtaining a neighbor entity of the entity in the knowledge graph as an entity context of the entity; and finally, through a false news detection model, fusing the news text with the entity and the entity context features to complete false news detection. According to the method, the ambiguity problem caused by entity mentionin news texts can be solved, and meanwhile, news representations of supplementary information, learning semantic level and knowledge level can be provided for entities in news.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, specifically relates to social network data, and proposes a false news detection method based on a knowledge-perceived attention network for news appearing in social media. Background technique [0002] Social media has become a platform for people to obtain and exchange information. Due to the ease of use of social media, more and more people use social media to obtain and publish news. At the same time, social media has gradually become an ideal platform for spreading fake news. Because fake news maliciously distorts and fabricates facts, its widespread dissemination will have a great negative impact on individuals and society. Therefore, detection of fake news on social media is urgent and beneficial to society. [0003] For fake news detection, early methods mainly extract and learn some features in news based on manually designed features, but the features learned in this way are no...

Claims

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Application Information

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IPC IPC(8): G06F16/951G06F16/9536G06F16/36G06F16/35G06F40/30G06F40/205G06F40/126G06Q50/00
CPCG06F16/951G06F16/9536G06F16/367G06F16/355G06F40/30G06F40/205G06F40/126G06Q50/01
Inventor 陈晨顿雅倩袁晓洁
Owner NANKAI UNIV
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