Network rumor detection method based on pre-trained language model

Abstract

The invention discloses a network rumor detection method based on a pre-trained language model. The network rumor detection method comprises the steps of obtaining a to-be-detected source text and forwarded texts of a plurality of other users; preprocessing the source text and forwarding texts of a plurality of other users respectively, and connecting the preprocessed forwarding texts to obtain aset of the forwarding texts; regarding a set of the preprocessed source text and the forwarded text as a pair of sentences; and constructing a linear sequence, inputting the linear sequence into the pre-trained language model, mining a semantic relationship between the source text and the forwarded text through the pre-trained language model, and obtaining the probability that the source text is arumor or a non-rumor through a full connection layer and a softmax function. According to the method, helpful high-level semantic features can be automatically learned and acquired without dependingon specific prior knowledge, so that the method has good generalization. The method does not need to depend on a large amount of forwarding / commenting information related to the source text, and earlydetection can be achieved.

Description

technical field [0001] The invention relates to the technical field of rumor detection, in particular to a method for detecting network rumors based on a pre-trained language model. Background technique [0002] With the development of Internet technology and the rise of smart terminal devices, social media platforms provide convenient channels for people to share a variety of news, and people can quickly upload massive multimedia data through simple operations. However, Internet rumors can also be used to spread widely and mislead the public. Bad rumors can guide public opinion, cause panic among the people, and affect the credibility of the government. Therefore, to ensure that users can get reliable news and maintain social order, detecting rumors on social media is an important task. [0003] Traditional online rumor detection methods rely on feature engineering, such as manually extracting features from user published messages and user personal information, and then a...

AI Technical Summary

Problems solved by technology

[0005] However, these methods have significant disadvantages

First, the text information on social media is short and concise, the grammar is irregular, the writing format is complicated, and semantic information is difficult to obtain; Forwarded on source events, not indirect, so semantic features on the time domain are insufficient and structural features are not available

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