The Impact of Mortality Salience, Negative Emotions and Cultural Values on Suicidal Ideation in COVID-19: A Conditional Process Model

Anthropomorphic Design in Mortality Salience Situations: Exploring Emotional and Non-Emotional Mechanisms Enhancing Consumer Purchase Intentions

This study investigates the role of anthropomorphic design in alleviating consumer anxiety induced by mortality salience and delves into the underlying emotional and non-emotional mechanisms. Through a series of meticulously designed experiments, we confirm that anthropomorphic design significantly enhances positive emotional responses in consumers, reduces negative emotions, and thereby increases their preference and willingness to purchase products. Even after the diminution of emotional reactions, anthropomorphic design continues to sustain consumer preference by enhancing psychological intimacy. These findings reveal the crucial role of anthropomorphic design as an effective emotional regulation strategy in consumer purchasing behavior, enriching the application of terror management theory and emotion regulation theory in consumer behavior research. Furthermore, our study provides valuable practical guidance for product design and marketing strategies, especially for consumer groups frequently facing high-pressure situations. Products with anthropomorphic designs may be more appealing to these consumers, helping to mitigate their death anxiety and enhance psychological well-being.

Does wealth equate to happiness? an 11-year panel data analysis exploring socio-economic indicators and social media metrics

The Easterlin paradox questions the link between economic growth and national well-being, emphasizing the necessity to explore the impact of economic elasticity, income inequality, and their temporal and spatial heterogeneity on subjective happiness. Despite the importance of these factors, few studies have examined them together, thus ongoing debates about the impact of economics on well-being persist. To fill this gap, our analysis utilizes 11 years of panel data from 31 provinces in China, integrating macroeconomic indicators and social media content to reassess the Easterlin paradox. We use GDP per capita and the Gini coefficient as proxies for economic growth and income inequality, respectively, to study their effects on the subjective well-being expressed by citizens on social media in mainland China. Our approach combines machine learning and fixed effects models to evaluate these relationships. Key findings include: (1) In temporal relationships, a 46.70% increase in GDP per capita implies a 0.38 increase in subjective well-being, while a 0.09 increase in the Gini coefficient means a 1.47 decrease in subjective well-being. (2) In spatial relationships, for every 46.70% increase in GDP per capita, subjective well-being rises by 0.51; however, this relationship is buffered by unfair distribution, and GDP per capita no longer significantly affects subjective well-being when the Gini index exceeds 0.609. This study makes a synthetic contribution to the debate on the Easterlin paradox, indicating that economic growth can enhance well-being if income inequality is kept below a certain level. Although these results are theoretically enlightening for the relationship between economics and national well-being globally, this study's sample comes from mainland China. Due to differences in cultural, economic, and political factors, further research is suggested to explore these dynamics globally.

The effects of short video app-guided loving-kindness meditation on college students' mindfulness, self-compassion, positive psychological capital, and suicide ideation

OBJECTIVE

The study investigated the effects of a short video app guided loving-kindness meditation (LKM) on college students' mindfulness, self-compassion, positive psychological capital, and suicide ideation. The purpose of the study is to investigate the intervention effect of LKM training on suicidal ideation among college students with the help of the short video application and to provide an empirical basis for the exploration of early suicide intervention strategies for college students.

METHODS

We recruited 80 college students from a university in China. The final 74 eligible participants were divided into two groups: app use group (n = 37) and the control group (n = 37). The app group accepted an 8-week app use interference, while the control group underwent no interference. We measured four major variable factors (mindfulness, self-compassion, positive psychological capital, and suicide ideation) before and after the app use intervention.

RESULTS

In the app group, self-compassion and positive psychological capital were significantly higher, and suicide ideation was significantly lower than the control group. In the control group, there were no noticeable differences in any of the four variables between the pre-test and post-test.

CONCLUSIONS

Our findings demonstrate that the short video app guided LKM may help to improve self-compassion, and positive psychological capital, and reduce suicide ideation. The finding of the short video app-guided LKM's effect extends our understanding of the integrative effects of positive psychology and digital media on the reduction of suicide ideation.

Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches

Depression is one of the most common mental illnesses but remains underdiagnosed. Suicide, as a core symptom of depression, urgently needs to be monitored at an early stage, i.e., the suicidal ideation (SI) stage. Depression and subsequent suicidal ideation should be supervised on social media. In this research, we investigated depression and concomitant suicidal ideation by identifying individuals' linguistic characteristics through machine learning approaches. On Weibo, we sampled 487,251 posts from 3196 users from the depression super topic community (DSTC) as the depression group and 357,939 posts from 5167 active users on Weibo as the control group. The results of the logistic regression model showed that the SCLIWC (simplified Chinese version of LIWC) features such as affection, positive emotion, negative emotion, sadness, health, and death significantly predicted depression (Nagelkerke's R2 = 0.64). For model performance: F-measure = 0.78, area under the curve (AUC) = 0.82. The independent samples' t-test showed that SI was significantly different between the depression (0.28 ± 0.5) and control groups (-0.29 ± 0.72) (t = 24.71, p < 0.001). The results of the linear regression model showed that the SCLIWC features, such as social, family, affection, positive emotion, negative emotion, sadness, health, work, achieve, and death, significantly predicted suicidal ideation. The adjusted R2 was 0.42. For model performance, the correlation between the actual SI and predicted SI on the test set was significant (r = 0.65, p < 0.001). The topic modeling results were in accordance with the machine learning results. This study systematically investigated depression and subsequent SI-related linguistic characteristics based on a large-scale Weibo dataset. The findings suggest that analyzing the linguistic characteristics on online depression communities serves as an efficient approach to identify depression and subsequent suicidal ideation, assisting further prevention and intervention.