Deep learning-based social network rumor detection method

A social network and deep learning technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as dimension explosion and difficult processing, and achieve the effects of improving efficiency, improving processing effect, and reducing complexity

Inactive Publication Date: 2017-09-19
BEIJING JIAOTONG UNIV
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Problems solved by technology

If all the sentences in a file are flattened into vectors, it will cause a dimension explosion and be difficult to handle

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  • Deep learning-based social network rumor detection method
  • Deep learning-based social network rumor detection method
  • Deep learning-based social network rumor detection method

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[0023] In order to illustrate the present invention more clearly, the present invention will be further described below in conjunction with preferred embodiments and accompanying drawings. Similar parts in the figures are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not limit the protection scope of the present invention.

[0024] Such as figure 1 As shown, the deep learning-based social network rumor detection method disclosed by the present invention comprises the following steps:

[0025] Collect social network data as sample data, sample data includes normal sample data and rumor sample data;

[0026] Mark and segment the sample data, build a dictionary with fixed-length numbers representing the sample words, and build a sample word vector with each number in the number of the sample words in the dictionary as an element;

[0027] The n...

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Abstract

The invention discloses a deep learning-based social network rumor detection method. The method comprises the steps of collecting social network data as sample data; performing marking and word segmentation on the sample data, and constructing a dictionary and sample word vectors having fixed lengths and taking numbers of sample words in the dictionary as elements; limiting a quantity of sample words contained in sample sentences of the sample data to be a fixed value; constructing a sample sentence matrix by adopting a Word2Vec method, wherein a row vector group of the sample sentence matrix is sample word vectors of all the sample words in the sample sentences; training the sample sentence matrix by adopting a deep learning method LSTM, and building a multilevel training model; constructing a to-be-detected sentence matrix by adopting a method as same as the method for constructing the sample sentence matrix; and performing classification detection on the to-be-detected sentence matrix according to the multilevel training model to obtain a rumor detection result of the to-be-detected social network data. According to the method, social network rumors can be effectively detected.

Description

technical field [0001] The invention relates to the technical fields of natural language processing and machine learning. More specifically, it involves a deep learning-based social network rumor detection method. Background technique [0002] In recent years, the spread of rumors in online social media has intensified. In the "China New Media Development Report" released by the Chinese Academy of Social Sciences in 2016, rumors on Weibo and WeChat were analyzed. Rumors spread on social media have caused social panic and instability, and also damaged the image of the country and the government. Therefore, how to effectively detect social network rumors and process them quickly is an urgent problem to be solved. [0003] At present, the detection of rumors on social networks is mainly based on manual inspection and keyword retrieval. Taking Sina Weibo as an example, the current handling of rumors mainly adopts the methods of user reporting, official investigation and manu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06N3/08G06Q50/00
CPCG06N3/08G06Q50/01G06F16/355G06F16/374
Inventor 解男男王星刘吉强王伟韩臻
Owner BEIJING JIAOTONG UNIV
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