The sum of all SCARES COVID-19 sentiment and asset return

Unleashing the link between the relaxation of the COVID-19 control policy and residents' mental health in China: the mediating role of family tourism consumption

Objective

COVID-19 has negatively influenced industrial development, family consumption, and residents' mental health. Unfortunately, it has not yet been studied whether this adverse situation can be alleviated after the relaxation of the COVID-19 control policy (RCC). Therefore, this study aimed to analyze the effect of the RCC on the resident's mental health and the mediating effect of family tourism consumption.

Methods

By using the PSM and mediating effetc model to research the panel data of two periods (April 2021 and April 2023) for Shaanxi province, China.

Results

The RCC negatively inhibited the mental health severity of residents, and the mental health severity decreased by 0.602. In particular, the RCC showed the most substantial negative effect on residents' stress, followed by anxiety and depression. Meanwhile, it is found that the impact of the RCC on the mental health of residents is highly heterogeneous. The RCC indicates a linear significant effect on the mental health of residents under 60 years of age, while the results were found insignificant for residents above 60 years of age. Meanwhile, the RCC's improvement effect on urban residents' mental health is greater than that of rural residents. In addition, mechanism analysis showed that tourism consumption plays a mediating role in the influence of the RCC on the mental health of residents, and the mediating effect accounted for 24.58% of the total effect.

Conclusion

Based on the findings, the study proposes that government and policymakers should strengthen mental health intervention, improve access to mental health counseling, stimulate economic development, expand the employment of residents, and track the mutation of the novel coronavirus.

COVID-19 related TV news and stock returns: Evidence from major US TV stations

We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic.

How social media expression can reveal personality

Background

Personality psychology studies personality and its variation among individuals and is an essential branch of psychology. In recent years, machine learning research related to personality assessment has started to focus on the online environment and showed outstanding performance in personality assessment. However, the aspects of the personality of these prediction models measure remain unclear because few studies focus on the interpretability of personality prediction models. The objective of this study is to develop and validate a machine learning model with domain knowledge introduced to enhance accuracy and improve interpretability.

Methods

Study participants were recruited via an online experiment platform. After excluding unqualified participants and downloading the Weibo posts of eligible participants, we used six psycholinguistic and mental health-related lexicons to extract textual features. Then the predictive personality model was developed using the multi-objective extra trees method based on 3,411 pairs of social media expression and personality trait scores. Subsequently, the prediction model's validity and reliability were evaluated, and each lexicon's feature importance was calculated. Finally, the interpretability of the machine learning model was discussed.

Results

The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the prediction model for personality traits ranged between 0.44 and 0.48 (p < 0.001). The correlation coefficients of five personality traits between the two "split-half" datasets data ranged from 0.84 to 0.88 (p < 0.001). Moreover, the model performed well in terms of contractual validity.

Conclusion

By introducing domain knowledge to the development of a machine learning model, this study not only ensures the reliability and validity of the prediction model but also improves the interpretability of the machine learning method. The study helps explain aspects of personality measured by such prediction models and finds a link between personality and mental health. Our research also has positive implications regarding the combination of machine learning approaches and domain knowledge in the field of psychiatry and its applications to mental health.