Social media rumor detection method and system based on hierarchical heterogeneous graph neural network

Abstract

The invention relates to a social media rumor detection method and system based on a hierarchical heterogeneous graph neural network, and the system comprises: a data preprocessing module which is used for preprocessing text data, extracting user static features, and constructing a hierarchical heterogeneous graph structure; an event encoding module which is used for encoding a text containing propagation and diffusion structure information; a user coding module which is used for learning user behavior features; a global heterogeneous graph coding module which is used for capturing rich global structure information between the event and the user; and a rumor detection label output module which is used for fusing the text information, the user behavior features and the global heterogeneous graph information to complete label prediction work of rumor detection. According to the method, local inline relationships between users and between texts can be effectively learned, user and text representations containing adjacent node information are generated, a global structure relationship between the user and an event is modeled and learned, and finally the authenticity of the event is identified.

Description

technical field [0001] The invention relates to the field of text detection, in particular to a social media rumor detection method and system based on a layered heterogeneous graph neural network. Background technique [0002] Rumor generally refers to a circulating statement or report whose authenticity has not been confirmed at the time of publication. This unverified statement may prove to be true, or may be partially or completely false, or its truth may not be verified for a long time. With the rapid development of social media platforms such as Twitter and Weibo, they have gradually replaced traditional media and become a convenient online platform for users to obtain information, express opinions and communicate with each other. Because social media has the essential characteristic of disseminating information at a high speed, while providing users with a quick way to obtain new information, it also provides a hotbed for the spread of rumors. Compared with traditio...

AI Technical Summary

Problems solved by technology

Existing research mainly focuses on the three key elements of rumor content, publishing users, and dissemination modes, especially focusing on text content modeling, mining the expression of uncertainty as an important clue to identify rumors, but for social media , which has typical characteristics such as short text information and irregular expressions, making it difficult for the model to effectively model events

In addition, the existing research regards the above three key elements as independent event representation elements, ignoring the interrelationship, mutual complementation and mutual enhancement relationship among them, that is, separating the "user-event" and "user-event". Users” are closely related, which leads to the limited performance of the rumor detection model, so it cannot accurately describe the rumor event

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