A Self-Fusion Network Based on Contrastive Learning for Group Emotion Recognition

Wang, Xingzhi; Zhang, Dong; Tan, Hongzhou; Lee, Dah-Jye

doi:10.1109/tcss.2022.3202249

Cited by 8 publications

(1 citation statement)

References 54 publications

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“…In the study of group emotions, Veltmeijer et al 10 reviewed the research on automatic group emotion recognition. Wang et al 11 proposed a framework consisting of three networks: FacesNet, SceneNet, and ObjectsNet, and utilized the information of faces, scene, and objects in images to identify group emotions. Yao et al 12 divided the development of public opinion into different stages based on the life cycle theory, revealing the evolution laws of group emotions in the phenomenon of topic resonance at different stages.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evolution Trend of Group Psychological Security Risks Under Public Health Emergencies: Mining and Analysis Based on Social Media Data

Gao,

Liu,

Sun

2024

PRBM

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Background: In the digital age, people's attitudes and psychological security towards public health emergencies will be shared. Similar or identical psychological security states are prone to clustering and differentiation, while differentiated group psychological security is more prone to polarization, leading to group psychological security risks and then posing a threat to social stability and national security. However, existing studies mostly use qualitative analysis methods to study group emotional risks. There are still limitations in the study of dynamics of group psychological security risks through mining real data of social media. Purpose:The study aims to use intelligent analysis methods to understand how group psychological security risks dynamically change. Methods:The study draws on text sentiment analysis, Markov chains and time series analysis to construct a framework for the evolution of group psychological security risks. Based on this framework, text data was crawled on Sina Weibo platform, mainly consisting of posts during public health emergencies (March 1st to June 30th, 2022) in Shanghai, and a psychological security lexicon in the field of public health emergencies was constructed. This laid the foundation for identifying the tendencies, intensity, and transitions of individual text psychological security, and then exploring the evolution trend of group psychological security risks. Results: Compared with the generation and reduction periods, group psychological security risks are more likely to occur during the outbreak and recovery periods, and the intensity level is also higher. The overall intensity of group psychological security risks shows an evolution trend of first increasing, then decreasing, and then increasing again. Conclusion:The paper provides an opportunity to explore the dynamics of psychological security in the digital space. Meanwhile, we call on the government and relevant management departments to pay more attention to the group psychological security risks formed during the outbreak and recovery periods of public health emergencies, and take corresponding measures in a timely manner to guide the public to transform the extreme psychological security state into the normal psychological security state, in order to prevent and resolve group psychological security risks, promote social stability and national security.

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Section: Introductionmentioning

confidence: 99%