emotional changes
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Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events.Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19.Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic.Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant “rebound effect” by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003).Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Abstrak Remaja merupakan proses peralihan dari anak anak menuju dewasa. Pada masa ini seseorang mengalami perubahan emosi yang signifikan dan tidak stabil. Biasanya remaja suka melakukan hal hal seenaknya sendiri tanpa mempertimbangkan dampak panjangnya contohnya seperti sering melakukan begadang. Bila begadang terlalu sering maka seseorang tersebut dapat mengalami berbagai gangguan kesehatan tubuh dan berpengaruh juga ke kesehatan mentalnya. Penelitian ini bertujuan untuk mengetahui dampak-dampak dari Begadang Terhadap kesehatan Mental Remaja serta memberikan jalan keluar dari gangguan untuk remaja yang terjebak dalam siklus begadang dan tidak dapat kembali ke jam normalnya Kata Kunci: Remaja, Begadang, Kesehatan Mental   Abstract Adolescence is a process of transition from children to adults. At this time a person experiences significant emotional changes and is unstable. Usually, teenagers like to do things on their own without considering the long-term impact, for example, often staying up late. If you stay up too often, a person can experience various health problems and affect their mental health. This study aims to determine the impacts of staying up late on the mental health of adolescents and to provide a way out of the disorder for adolescents who are trapped in the cycle of staying up late and unable to return to their normal hours. Keywords: Teenagers, Staying Up Late, Mental Health

Since indoor, sedentary lifestyles became prevalent in society, humans have lost a sustainable connection to nature. An intervention utilizing outdoor horticultural activities could address such a challenge, but their beneficial effects on the brain and emotions have not been characterized in a quantitative approach. We aimed to investigate brain activity and emotional changes in adults in their 20s during horticultural activity to confirm feasibility of horticultural activity to improve cognitive and emotional states. Sixty university students participated in 11 outdoor horticultural activities at 2-min intervals. We measured brain waves of participants’ prefrontal cortex using a wireless electroencephalography device while performing horticultural activities. Between activities, we evaluated emotional states of participants using questionnaires. Results showed that each horticultural activity showed promotion of brain activity and emotional changes at varying degrees. The participants during physically intensive horticultural activities—digging, raking, and pruning—showed the highest attention level. For emotional states, the participants showed the highest fatigue, tension, and vigor during digging and raking. Plant-based activities—harvesting and transplanting plants—made participants feel natural and relaxed the most. Therefore, this pilot study confirmed the possibility of horticultural activity as a short-term physical intervention to improve attention levels and emotional stability in adults.

The future emotion prediction of users on social media has been attracting increasing attention from academics. Previous studies on predicting future emotion have focused on the characteristics of individuals’ emotion changes; however, the role of the individual’s neighbors has not yet been thoroughly researched. To fill this gap, a surrounding-aware individual emotion prediction model (SAEP) based on a deep encoder–decoder architecture is proposed to predict individuals’ future emotions. In particular, two memory-based attention networks are constructed: The time-evolving attention network and the surrounding attention network to extract the features of the emotional changes of users and neighbors, respectively. Then, these features are incorporated into the emotion prediction task. In addition, a novel variant LSTM is introduced as the encoder of the proposed model, which can effectively extract complex patterns of users’ emotional changes from irregular time series. Extensive experimental results show that the proposed approach outperforms five alternative methods. The SAEP approach has improved by approximately 4.21–14.84% micro F1 on a dataset built from Twitter and 7.30–13.41% on a dataset built from Microblog. Further analyses validate the effectiveness of the proposed time-evolving context and surrounding context, as well as the factors that may affect the prediction results.

AbstractParkinson’s disease (PD) is a multi-system neurodegenerative disorder. Patients with PD often suffer chronic pain. In the present study, we investigated motor, sensory and emotional changes in three different PD mice models. We found that 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treatment caused significant changes in all measurements. Mechanical hypersensitivity of PD model induced by MPTP peaked at 3 days and persisted for at least 14 days. Using Fos transgenic mice, we found that neurons in the anterior cingulate cortex (ACC) were activated after MPTP treatment. Inhibiting ACC by bilateral microinjection of muscimol significantly reduced mechanical hypersensitivity and anxiety-like responses. By contrast, MPTP induced motor deficit was not affected, indicating ACC activity is mostly responsible for sensory and emotional changes. We also investigated excitatory synaptic transmission and plasticity using brain slices of MPTP treated animals. While L-LTP was blocked or significantly reduced. E-LTP was not significantly affected in slices of MPTP treated animals. LTD induced by repetitive stimulation was not affected. Furthermore, we found that paired-pulse facilitation and spontaneous release of glutamate were also altered in MPTP treated animals, suggesting presynaptic enhancement of excitatory transmission in PD. Our results suggest that ACC synaptic transmission is enhanced in the animal model of PD, and cortical excitation may play important roles in PD related pain and anxiety.

(1) Background: in early 2020, COVID-19 broke out. Driven by people’s psychology of conformity, panic, group polarization, etc., various rumors appeared and spread wildly, and the Internet became a hotbed of rumors. (2) Methods: the study selected Weibo as the research media, using topic models, time series analysis, sentiment analysis, and Granger causality testing methods to analyze the social media texts related to COVID-19 rumors. (3) Results: in study 1, we obtained 21 topics related to “COVID-19 rumors” and “outbreak rumors” after conducting topic model analysis on Weibo texts; in study 2, we explored the emotional changes of netizens before and after rumor dispelling information was released and found people’s positive emotions first declined and then rose; in study 3, we also explored the emotional changes of netizens before and after the “Wuhan lockdown” event and found positive sentiment of people in non-Wuhan areas increased, while negative sentiment of people in Wuhan increased; in study 4, we studied the relationship between rumor spread and emotional polarity and found negative sentiment and rumor spread was causally interrelated. (4) Conclusion: These findings could help us to intuitively understand the impact of rumors spread on people’s emotions during the COVID-19 pandemic and help the government take measures to reduce panic.