Predicting Retweeting Behavior Based on BPNN in Emergency Incidents

Ding, Xuejun; Tian, Yong

doi:10.1142/s0217595917400115

Cited by 7 publications

(7 citation statements)

References 5 publications

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“…Also, each neuron receives the information sent by the neurons in the previous layer and transmits the received information through nonlinear transformation. e neural network can approach complex mapping relations through training and has wide applications in pattern recognition, function approximation, and classification prediction [13]. BPNN consists of three parts: input layer, hidden layer, and output layer.…”

Section: Methodsmentioning

confidence: 99%

“…In equation (13), Y i and Y j are the standardized values of the observed values of spatial unit i and unit j, respectively, and W ij indicates the spatial weight. Local spatial autocorrelation can analyze the relationship between a certain point and the surrounding points of the cluster, including highhigh clustering, high-low clustering, and low-high clustering.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

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Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm Model in Big Data Era

2021

Scientific Programming

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In recent years, due to the rapid development of rural tourism, rural tourism has lost its unique rurality, which has led to a certain impact on the sustainable development of rural tourism. Primarily, based on the rural characteristics, the social environment development, population development, and economic development are taken as the research indexes, and the evaluation index system of rural tourism destination is constructed. Afterward, an empirical study on the spatial pattern of rural tourism is carried out with examples, and the model is simulated and analyzed by MATLAB software. Finally, the spatial autocorrelation method is used to analyze the evolution characteristics of the rural tourism spatial pattern. The results show that through the analysis of the evaluation error curve of the Back Propagation Neural Network (BPNN), the evaluation error and the actual error range are within 0.08%, which proves that the BPNN algorithm has good calculation accuracy. The BPNN rural tourism destination rurality evaluation model established here can make an effective evaluation of rural tourism space. The results show that the proportion of employees in the primary industry and the penetration rate of mobile phones are the decisive factors in the adjustment of industrial structure and social environmental factors, respectively. Rural per capita tourism income and the proportion of primary industry output value will also have a certain impact on rural evolution. Certain guiding significance is provided for the sustainable development of rural tourism.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm Model in Big Data Era

2021

Scientific Programming

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“…(1) e nonlinear mapping of inputs and outputs is realized; that is, neural networks can be used to approach any nonlinear continuous functions, which are suitable for the multidimensional feature construction in data mining, and the BPNN can use the gradient descent algorithm to optimize the parameters and reduce errors [15].…”

Section: Strengthsmentioning

confidence: 99%

“…In equation (15), q(a k ) is the accumulation probability of individual a k . e next generation of new populations will be obtained after the previous step.…”

Section: Establishment and Optimization Of The Modelmentioning

confidence: 99%

Evaluation of the Online Music Flipped Classroom under Artificial Intelligence and Wireless Networks

Tan

Cao

2022

Wireless Communications and Mobile Computing

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The study aims to explore the online music flipped classrooms based on artificial intelligence (AI) and wireless networks. A backpropagation neural network (BPNN) algorithm optimized by the genetic algorithm (GA) is proposed to evaluate the teaching quality of music flipped classrooms and analyze the problems in the current teaching mode. First, an evaluation index system is established for online music flipped classrooms; second, a questionnaire is designed according to the index system. After the data are collected, the GA-BPNN evaluation model is used to evaluate the teaching quality of the music flipped classrooms. Finally, the model’s performance is evaluated based on the forecast accuracy compared with the model implemented only by the BPNN. The simulation results show that the GA-BPNN evaluation model can effectively evaluate the teaching quality of flipped classrooms, and the evaluation results are objective and accurate. The model overcomes the shortcomings of traditional evaluation methods. The study has great practical significance and provides a basis for improving the teaching quality of online flipped classrooms.

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“…Chen et al propose a semi-supervised graph model (SGM) to predict the retweeting behavior by detecting users' emotional status corresponding with their current mood from their friends' tweets, then using Learn-to-Rank method, the Top-N retweets are obtained [10]. Ding and Tian build a model based on a back propagation neural network (BPNN) to predict the retweeting behavior of social media users, which extracts 11 feature vectors from recipient characteristics, retweeter characteristics, tweet content characteristics, and external media coverage [11]. Jiang et al employ one-class collaborative filtering method to predict user's retweeting behavior by quantitatively measure the individual preference and social influence [12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

et al. 2020

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Rumors in social media not only affect the health of online social networks, but also reduce the quality of information accessed by social media users. When emergencies occur, the rapid spread of rumors can even trigger mass anxiety and panic. However, the existing studies did not make a clear distinction between rumor and non-rumor information in public emergencies, so that they cannot effectively predict the rumor retweeting behavior. To this end, a model for predicting rumor retweeting behavior is presented based on the convolutional neural networks (CNN) called R-CNN model in the paper. In this model, the rumor retweeting behavior is considered as an important driving force of increasing the depth and breadth of rumor cascades, and four feature vectors are constructed with the historical textual content published by users, consisting of attention to public emergencies, attention to rumors, reaction time and tweeting frequency. To input the quantitative feature vectors for R-CNN, a K-means based core tweets extraction method is proposed to select the right tweets, and the quantitative feature representations are proposed. The predictive capability of the model has been proved by experiments base on two rumor datasets of emergencies crawled from Sina weibo. Experimental results indicate that the prediction accuracy of the model reaches 88%, and it can be improved by 7% on average compared with other models. INDEX TERMS Behavior prediction, convolutional neural networks, public emergency, rumor.

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Predicting Retweeting Behavior Based on BPNN in Emergency Incidents

Cited by 7 publications

References 5 publications

Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm Model in Big Data Era

Evaluation of Rural Tourism Spatial Pattern Based on Multifactor-Weighted Neural Network Algorithm Model in Big Data Era

Evaluation of the Online Music Flipped Classroom under Artificial Intelligence and Wireless Networks

Predicting Rumor Retweeting Behavior of Social Media Users in Public Emergencies

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