Chinese microblog rumor detection based on deep sequence context

Lin, Dazhen; Ma, Ben; Cao, Donglin; Li, Shaozi

doi:10.1002/cpe.4508

Cited by 15 publications

(16 citation statements)

References 17 publications

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“…e rumor detection module is used to extract features, and the checkpoint module is used to solve the problem of timeliness, which is used to trigger the rumor detection module to ensure the timeliness of rumor detection while ensuring accurate identification of rumors. Lin et al [15] raised the issue of word independence and found that some common words appear in rumors. Once these words appear, they can be judged as rumors.…”

Section: Related Workmentioning

confidence: 99%

A New Early Rumor Detection Model Based on BiGRU Neural Network

Chen

Wang

Dong

et al. 2021

Discrete Dynamics in Nature and Society

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With the progress of society and the rapid development of computer technology, rumors arise on social media, which seriously affects the social economy. How to detect rumors accurately and rapidly has become one hot research topic. In this paper, a new early rumor detection model is proposed. The aim of this model is to increase the efficiency and the accuracy of rumor detection simultaneously. Specifically, in this model, the input data is firstly refined through account filtering and data standardization, then the BiGRU is used to consider the context relationship, and a reinforcement learning algorithm is applied to detection. Experimental results show that compared with other early rumor detection models (e.g., checkpoints), the accuracy of the proposed model is improved by 0.5% with the same speed, which testifies the effectiveness of this model.

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Section: Related Workmentioning

confidence: 99%

A New Early Rumor Detection Model Based on BiGRU Neural Network

Chen

Wang

Dong

et al. 2021

Discrete Dynamics in Nature and Society

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“…A deep learning approach can learn from the simple inputs of contexts and changes in the content of hidden representations. For detecting rumors in Chinese microblogs, Lin et al 3 proposed a sequence model with RNNs, wherein falsity and influence were the two key factors in each rumor. To better understand a rumor, their model captured the bi‐directional sequence content.…”

Section: Related Workmentioning

confidence: 99%

“…However, the low concurrency of multilayer RNNs will lead to long execution time both in the training and testing phases. The popularity of deep learning has warranted the need for research 2,3 using RNNs, which has significantly reduced the efficiency of the models 2,3 . A lack of concurrency indicates that more time and resources are required during the training phase.…”

Section: Introductionmentioning

confidence: 99%

Social rumor detection based on multilayer transformer encoding blocks

Lin

Chen

2020

Concurrency and Computation

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The propagation of rumors on social media has been identified as a critical problem in recent years that causes social panic or economic turmoil (to some extent), thereby giving rise to the need for faster identification. With the advancements in deep learning, researches based on neural networks become popular. Most of the existing methods extensively adopt recurrent neural networks (RNNs), such as gated recurrent unit and long short-term memory. This results in a significant degradation in the concurrency performance of the models, implying an increased consumption of time and resources. This study proposes a model with multilayer transformer encoding blocks for detecting rumors. The self-attention mechanism in the transformer encoding blocks provides better concurrency to the proposed model and improves its performance. The proposed model performs faster executions using less or no recurrences in comparison to other models based on RNNs. Experiments on two real social datasets verified that our model could achieve significantly better results than baseline methods. The accuracy rate increased by 1.1% and 2.2% on the Weibo and PHEME datasets, respectively. In comparison to the methods that use RNNs as a feature extractor, the training duration in the proposed model was reduced by 16% and 70% in Weibo and PHEME, respectively.

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“…The first systematic and scientific study of rumors began in 1942, 1 and then with the constant change of rumor propagation platform and the development of computer technology, the research theory of rumor propagation has changed from the initial applied mathematical theory and applied physics theory to the current applied computer theory and complex network theory. With the rapid development of mobile social network platform, 2,3 the most valuable research theory is undoubtedly the latest complex network theory. However, in the current researches on rumor propagation based on complex networks, traditional complex network models, such as Barabási–Albert (BA) model and Watts–Strogatz (WS) model, are mostly used as experimental networks.…”

Section: Introductionmentioning

confidence: 99%

CSRT rumor spreading model based on complex network

Hong

Zheng

et al. 2021

Int J Intell Syst

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Rumors mislead judgments of people, affect economic development, and the stability of social order. The research on the rule of spreading rumors is significant and meaningful. This paper improves the traditional Barabási–Albert scale‐free network and proposes a network topology model that conforms to the characteristics of sharing social networks based on the complex network theory and the actual characteristics of sharing social networks. In addition, the credulous spider rational taciturn rumor propagation model is proposed by improving the credulous spider rational model, which solves the overspread problem of the traditional rumor propagation model. This paper further studies the influence of anxiety on the spread of rumors, and finds that the anxiety of audience is increasing with the spread degree of rumors.

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Chinese microblog rumor detection based on deep sequence context

Cited by 15 publications

References 17 publications

A New Early Rumor Detection Model Based on BiGRU Neural Network

A New Early Rumor Detection Model Based on BiGRU Neural Network

Social rumor detection based on multilayer transformer encoding blocks

CSRT rumor spreading model based on complex network

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