User Behavior Prediction of Social Hotspots Based on Multimessage Interaction and Neural Network

Xiao, Yunpeng; Li, Jinghua; Zhu, Yangfu; Li, Qian

doi:10.1109/tcss.2020.2969484

Cited by 16 publications

(3 citation statements)

References 30 publications

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“…When compared to previous models, the suggested method enhanced prediction precision by 27%, 23.5 percent accuracy, as well as 20 percent recall rate, indicating that the model efficiently anticipated hot events. Other similar methods also work on identifying "social hotspots" such as Krishna et al [23] and Xiao et al [19]. Alberto Rossi, et al [4] develops an attention mechanism as well as a LSTM network -RNN method for modelling taxi driver performance and storing the semantics of famous attractions in order to anticipate a cab's next destination using spatial location from LBSNs.…”

Section: Literature Surveymentioning

confidence: 99%

Cross-Event User Reaction Prediction on a Social Network Platform

Bide¹,

Dhage²

2022

IJACSA

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Social network surges with multiple tweets with mixture of multiple emotions by many users when events like rape, robbery, war and murder, we use this user data to analyze user emotions between cross-events and try to predict user reactions for the next possible such event. Cross-events are a series of events that belong under the same umbrella of topics and are related to the events occurring prior to it. The proposed system solve this problem using collaborative filtering using Topical and Social context. The Text Rank Algorithm is an unsupervised algorithm used for keyword extraction. Count Vectorizer is used on preprocessed text to get the frequency of words throughout the text which is used as training data to get a probability of emotion using a logistic regression model. We incorporated social context along with topical context to account for homophily and used the Low-rank matrix factorization method for user-topic prediction. The model as an output gives a total of 8 emotions which include Shame, Disgust, Anger, Fear, Sadness, Neutral, Surprise and Joy. Finally, the model is able to predict emotions with an accuracy of 95% considering cross events.

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Section: Literature Surveymentioning

confidence: 99%

Cross-Event User Reaction Prediction on a Social Network Platform

Bide¹,

Dhage²

2022

IJACSA

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“…In recent years, scholars have proposed some information spreading models, but these models still have some shortcomings. First of all, they only consider the single effect of a single action such as user forwarding or commenting on information [22], ignoring the multiple effects of actions, for example, a user may both forward and comment on the information. Due to the diversity of behaviors taken by users, it is necessary to study the impact of different behaviors taken by users on the process of information spread.…”

Section: Introductionmentioning

confidence: 99%

A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

Zhang

Lin

Wang

et al. 2023

IEEJ Transactions Elec Engng

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Online social networks (OSNs) provide a platform for users to express opinions, discuss events and exchange ideas. However, the spread of misinformation in OSNs will interfere with users' judgment of useful information and may even cause significant economic losses to society. Exploring the spreading mechanism of misinformation in online social networks is the basis of eliminating the harm brought by misinformation to the network. Firstly, considering the influence of time delay on information spreading in real life, we propose a new misinformation spreading model to explore its spreading mechanism and describe its spreading process. Secondly, we provide the predictive method for studying the spread of misinformation in OSNs and theoretically demonstrate the stability of equilibrium points in the proposed information spreading model. Finally, we conduct simulation experiments based on a real dataset. The results show that compared to the benchmark model, our proposed model can reasonably explore the spreading mechanism and accurately predict the spreading trend of misinformation. Our proposed model and method can further establish a foundation for fast and effectively controlling the spread of misinformation and improving the controllability and efficiency of network communication and information spread. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…It is sensitive to weights and thresholds, and it is easy to fall into a local optimal solution. Xiao et al [6] proposed a BP neural network prediction model based on simulated annealing algorithm, which reduced the possibility of over-fitting of neural network and improved the prediction accuracy. On the basis of BP neural network, Hou et al [7] adopted particle swarm optimization algorithm to optimize it, and put forward PSO-BP prediction model, which improved the accuracy of prediction.…”

Section: Introductionmentioning

confidence: 99%

LSALO-BP: A Hybrid Model for Children Adulthood Height Prediction

Mao

Chen

Fan

et al. 2022

Preprint

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The prediction of children's adult height is a common procedure in childhood endocrinology. Through the prediction of children's adult height, it is possible to find abnormalities in children's growth and development. Many jobs in today's society have certain requirements for height, so the accuracy of children adulthood height prediction is important for children. Current methods for predicting adult height of children have some shortcomings such as inaccurate accuracy. To deal with these problems, this paper analyzes the data collected by the Chinese children and adolescents' physical and growth health projects in primary and secondary schools in Zhejiang Province, and proposes a method for predicting adult height based on back propagation neural network (BPNN) with the body composition of children and adolescents as input. Since the BP algorithm has the risk of falling into local optimization, and we propose LSALO-BP model that incorporates the ant lion optimizer (LSALO) into the BP algorithm as location strategy to avoid local optimization. The improvements achieved by the ant lion algorithm are mainly reflected in: improving the ant's walk mode, and enhancing the global search ability of the LSALO algorithm. The comparison experiment of 10 benchmark functions proves the feasibility and effectiveness of the location strategy. The LSALO-BP model is applied to the prediction of adult height of children and adolescents. The experimental results show that compared with other models, the LSALO-BP prediction model has increased the prediction accuracy by 6.67%~16.08% for boys and 4.67%~6.6% for girls, which can more accurately predict the adult height of children and adolescents.

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User Behavior Prediction of Social Hotspots Based on Multimessage Interaction and Neural Network

Cited by 16 publications

References 30 publications

Cross-Event User Reaction Prediction on a Social Network Platform

Cross-Event User Reaction Prediction on a Social Network Platform

A Trend Prediction Method for Misinformation Spreading with Time Delay Effect

LSALO-BP: A Hybrid Model for Children Adulthood Height Prediction

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