Social media rumor detection method based on multi-task learning

Abstract

The invention relates to a social media rumor detection method based on multi-task learning, and the method specifically comprises the following steps: S1, carrying out the data extraction and format conversion of a corpus, and obtaining the source, reply and propagation path of a post; s2, extracting features of a line style; s3, extracting features of user confidence; s4, performing text preprocessing on text parts in the source post and the reply post to input subsequent tasks; s5, performing vector splicing on the features extracted in the S2 and the S3 and the text representation in the S4; s6, putting the spliced vector into a shared BERT layer; s7, respectively constructing neural network structures; and S8, inputting the data processed in the S5 into a neural network structure, and outputting standing classification and rumor classification. According to the method, a multi-task joint model can be used for being combined with two highly-related tasks, rumor detection and site classification tasks are improved, and rumor detection performance is improved.

Description

technical field [0001] The invention relates to the technical field of rumor detection, in particular to a multi-task learning-based social media rumor detection method. Background technique [0002] In recent years, with the rapid development of social media, people tend to check relevant news they care about through social media such as twitter and reddit. However, while these social media provide convenience to our life, they also lead to the problem of flooding of information and spreading rumors on the Internet. Rumors have brought a lot of harm to people's production and life. Viral rumors often arouse public opinion, disrupt social order, and bring negative impacts on social economy and politics. At the same time, rumors can also affect people's judgment. [0003] The bad influence of rumors has aroused widespread public concern, and rumor detection technology needs to be improved urgently. The task of rumor detection on social media is challenging since traditiona...

AI Technical Summary

Problems solved by technology

The task of rumor detection on social media is challenging since traditional news media detection algorithms are ineffective or inapplicable to the task of rumor detection in social media, and it is difficult to detect cases where rumors are intentionally written to mislead readers.

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