Phase Transition in Group Emotion

Yu, Xuelong; Nian, Fuzhong; Yao, Yabing; Luo, Li

doi:10.1109/tcss.2021.3073899

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…[3][4][5] For example, in the outbreak of the new coronavirus epidemic in 2020, social media platforms have become the main battlefield for hundreds of millions of Weibo users to obtain epidemic information, and also the gathering place for hundreds of millions of Weibo users to express their psychological security. However, after public with similar or identical psychological security states gather, it is easy to trigger group psychological security differentiation, and the differentiated group psychological security is more prone to polarization (that is, excessive group security or excessive group insecurity), 6,7 inducing group psychological security risks. For example, excessive group security can easily lead to group slackness, numbness, and apathy, and even inability to respond rationally in crisis situations, further hiding risks.…”

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%

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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“…Holme et al [38] suggested that a continuous phase transition is experienced from majority opinion diversity to majority sharing the same opinion. Yu et al [39] investigated the phenomenon of group emotion phase transition from two aspects: without network structure and based on the synergistic evolution of emotion and network structure.…”

Section: Introductionmentioning

confidence: 99%

Topological phase transition in network spreading

Nian

Zhang²

2023

Chinese Phys. B

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This paper investigates information spreading from the perspective of topological phase transition. Firstly, a new hybrid network is constructed based on the small-world networks and scale-free networks. Secondly, the attention mechanism of online users in information spreading is studied from four aspects: social distance, individual influence, content richness, and individual activity, and a dynamic evolution model of “connecting with spreading” is designed. Eventually, numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution model. The simulation results show that topological structure and node influences in different networks have undergone phase transition, which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short period. The infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution rule. Furthermore, the simulation results are compared with the real data, which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks, verifying the validity of the model proposed in this paper.

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基于信息传播的动态自相似kc中心网络

Wang

Zhang

Yao

et al. 2022

J. Shanghai Jiaotong Univ. (Sci.)

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In this paper, we explore a principal way to enhance the quality of object masks produced by different segmentation models. We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process. As a result, the refinement process can be smoothly implemented through a series of denoising diffusion steps. Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. By predicting the label and corresponding states-transition probabilities for each pixel, SegRefiner progressively refines the noisy masks in a conditional denoising manner. To assess the effectiveness of SegRefiner, we conduct comprehensive experiments on various segmentation tasks, including semantic segmentation, instance segmentation, and dichotomous image segmentation. The results demonstrate the superiority of our SegRefiner from multiple aspects. Firstly, it consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks. Secondly, it outperforms previous model-agnostic refinement methods by a significant margin. Lastly, it exhibits a strong capability to capture extremely fine details when refining high-resolution images. The source code and trained models are available at github.com/MengyuWang826/SegRefiner.

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Phase Transition in Group Emotion

Cited by 10 publications

References 46 publications

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

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

Topological phase transition in network spreading

基于信息传播的动态自相似kc中心网络

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