Microblog rumor detection method

Abstract

The invention provides a microblog rumor detection method, which considers an attention mechanism. The method comprises the following steps of collecting a microblog event and a corresponding commentdata set as sample data; preprocessing the sample data, and respectively extracting text contents of original microblogs and comments; pre-training the text by adopting a BERT pre-training model, andgenerating a sentence vector with a fixed length for each sentence of text; constructing a dictionary, and extracting original microblogs and a plurality of corresponding comments to form a microblogevent vector matrix; training the vector matrix by adopting a deep learning method Text CNN-Attention, and constructing a multi-level training model; and performing classification detection on the vector matrix according to the multi-level training model to obtain a rumor detection result corresponding to the social network data. Compared with a traditional rumor detection method, accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a microblog rumor detection method. Background technique [0002] Rumors generally refer to unverified statements or statements, often related to an event. With the rapid development of social media, rumors can spread rapidly through social media at the speed of nuclear fission. Microblog, one of social media, is a new type of open Internet social service in the Web2.0 era. Users can update their microblogs with short text anytime and anywhere with the help of the Internet or mobile phones and other media, and share information with more users. Compared with traditional blogs, Weibo has the characteristics of communication: instant sharing of blog posts, innovative interactive methods, and vivid live performances. In terms of communication effect, it shows: popularity accumulation, economical and fast brand marketing. However, in the diversifie...

AI Technical Summary

Problems solved by technology

At present, most of the pre-training models used in the research on rumor detection by constructing neural networks through deep learning are word2vec word vectors or ELMo, but the word vectors obtained in the former cannot solve the problem of polysemous words, so that each word trained can only correspond to one Vector representation, and the latter can dynamically adjust the word embedding according to the context, but use LSTM for feature extraction instead of Transformer, and ELMo uses context vector splicing as the current vector, so the fused vector features are poor

The training model mostly uses CNN or RNN network, but although the CNN network can extract the semantic features, it ignores the contextual word order features, and the CNN network cannot distinguish the more obvious features when splicing the pooled features after the full connection operation

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