Stochastic model for emotion contagion in social networks security based on machine learning

Fei, Dingzhou

doi:10.1016/j.ssci.2019.06.004

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…One of the reasons is that we underestimated the amount of information flowing through social media. Social media in particular amplifies public emotions and disseminates this information to various nodes and spreads at an extremely fast speed [ 9 ]. Previous studies have shown that social media provides a source for the public in crisis to quickly find necessary information [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining

2022

Computational and Mathematical Methods in Medicine

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Background. Since the COVID-19 pandemic, social media has become an important arena for the public to transmit and exchange messages, feelings, opinions, and information about the epidemic. In the era of social media, many UGC contents from self-media and various information about the epidemic on social media have strong emotional colors. These contents are not only rich in resources for text sentiment analysis but also reveal the laws and characteristics of the evolution of users’ emotional tendencies in public health emergencies. It even ties together the interaction between media content and society. Objective. As the Sina Weibo platform’s characteristics of communication are real-time, open, and “many-to-many,” the objective of this study is to collect Weibo-blog contents tagged with the outbreak of COVID-19 in a certain metropolis in China and analyze the emotional evolution situation of Weibo-blogs of the unexpected public health emergency involved. This will provide a dynamic understanding of the mechanisms underlying the evolution of emotional conditions in the context of public health emergencies. Methods. This paper uses a Python crawler, the SnowNLP sentiment analysis model, and correlation analysis to calculate the emotional tendencies of the event “Covid-19 outbreak in a Chinese metropolis” on the Sina Weibo platform. The study was carried out in terms of the evolutionary stages of the event and the factors which induce it. Results. This paper revealed characteristics of time-varying laws and dynamic propagation of users’ emotional evolution in public health emergencies. (1) This study refers to the life cycle model of COVID-19, combined with the statistics of the time series of the quantities of Weibo-blog posting, and divides the law of the quantities of Weibo-blog posting changing with the event into three stages: outbreak period, stalemate period, and resolution period. (2) Users’ emotional tendencies are changeable and unstable which are easily induced by various factors. (3) There is a significant positive correlation between the reported confirmed cases and the quantities of Weibo-blog posts. (4) Individual emotional tendencies will have a positive changing trend with the public’s average emotional tendencies after the event occurs. (5) There is no correlation between reposts, comments, and Weibo-blog emotional tendencies. Conclusion. The research found that, given staged evolution and repeated fluctuations of emotional tendencies, relevant departments should effectively use this law and set up different response plans according to different stages. In addition, what is highly coupled with users’ emotional tendencies is not only the information about the virus but more large-scale infected by different intensities of emotions.

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Section: Discussionmentioning

confidence: 99%

Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining

2022

Computational and Mathematical Methods in Medicine

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“…To select key features that can summarize the characteristics of facial expression changes, first, the facial movement units should be analyzed, the data of the multiple geometric shapes between facial feature points in the sequential images should be calculated, and these data need to be mapped to figures and tables for comparative analysis [23,24]. Then, according to the characteristics of the data changes in the images, features with more obvious changes should be selected; after that, through analysis, changes of facial feature points should be extracted to reflect the changes in facial features, so as to improve the expression recognition rate [25].…”

Section: Fig 2 Facial Expression Feature Extraction Methodsmentioning

confidence: 99%

Machine Learning-Based Student Emotion Recognition for Business English Class

Cui

Wang

Zhao

2021

Int. J. Emerg. Technol. Learn.

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Traditional English teaching model neglects student emotions, making many tired of learning. Machine learning supports end-to-end recognition of learning emotions, such that the recognition system can adaptively adjust the learning difficulty in English classroom. With the help of machine learning, this paper presents a method to extract the facial expression features of students in business English class, and establishes a student emotion recognition model, which consists of such modules as emotion mechanism, signal acquisition, analysis and recognition, emotion understanding, emotion expression, and wearable equipment. The results show that the proposed emotion recognition model monitors the real-time emotional states of each student during English learning; upon detecting frustration or boredom, machine learning will timely switch to the contents that interest the student or easier to learn, keeping the student active in learning. The research provides an end-to-end student emotion recognition system to assist with classroom teaching, and enhance the positive emotions of students in English learning.

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A communication security anti-interference decision model using deep learning in intelligent industrial IoT environment

Yan

Wang

et al. 2022

Soft Comput

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Stochastic model for emotion contagion in social networks security based on machine learning

Cited by 11 publications

References 18 publications

Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining

Sentiment Analysis of Texts on Public Health Emergencies Based on Social Media Data Mining

Machine Learning-Based Student Emotion Recognition for Business English Class

A communication security anti-interference decision model using deep learning in intelligent industrial IoT environment

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