Background: Despite worldwide calls for precautionary measures to combat COVID-19, the public’s preventive intention still varies significantly among different regions. Exploring the influencing factors of the public’s preventive intention is very important to curtail the spread of COVID-19. Previous studies have found that fear can effectively improve the public’s preventive intention, but they ignore the impact of differences in cultural values. The present study examines the combined effect of fear and collectivism on the public’s preventive intention towards COVID-19 through the analysis of social media big data.Methods: The Sina microblog posts of 108,914 active users from Chinese mainland 31 provinces were downloaded. The data was retrieved from January 11 to February 21, 2020. Afterwards, we conducted a province-level analysis of the contents of downloaded posts. Three lexicons were applied to automatically recognise the scores of fear, collectivism, and preventive intention of 31 provinces. After that, a multiple regression model was established to examine the combined effect of fear and collectivism on the public’s preventive intention towards COVID-19. The simple slope test and the Johnson-Neyman technique were used to test the interaction of fear and collectivism on preventive intention.Results: The study reveals that: (a) both fear and collectivism can positively predict people’s preventive intention and (b) there is an interaction of fear and collectivism on people’s preventive intention, where fear and collectivism reduce each other’s positive influence on people’s preventive intention.Conclusion: The promotion of fear on people’s preventive intention may be limited and conditional, and values of collectivism can well compensate for the promotion of fear on preventive intention. These results provide scientific inspiration on how to enhance the public’s preventive intention towards COVID-19 effectively.

Based on the experiential avoidance model, the current study aims to test the relationship between cyber-ostracism and adolescents’ non-suicidal self-injury and to explore the mediating roles of depression and experiential avoidance. A sample of 1062 middle school students completed questionnaires on cyber-ostracism, depression, experiential avoidance, and self-injurious behavior. The results showed that cyber-ostracism, depression, experiential avoidance, and non-suicidal self-injury were positively correlated with each other. After controlling for gender and age, the mediation model test shows that cyber-ostracism was significantly and positively associated with non-suicidal self-injury. Depression and experiential avoidance mediated the relationship between cyber-ostracism and non-suicidal self-injury parallelly and sequentially. This study highlights the potential mechanisms of action between cyber-ostracism and adolescent non-suicidal self-injury and finds that cyber-ostracism is a risk factor for non-suicidal self-injury. This founding suggests that extra attention should be paid to the role of the online environment in addition to the offline environment experiences for the intervention of non-suicidal self-injury.

This study explores an innovative approach to predicting individual life satisfaction by combining large language models (LLMs) with machine learning (ML) techniques. Traditional life satisfaction assessments rely on self-report questionnaires, which can be time-consuming and resource intensive. To address these limitations, we developed a method that utilizes LLMs for feature extraction from open-ended self-statement texts, followed by ML prediction. We compared this approach with standalone LLM predictions and expert ratings. A sample of 378 participants completed the satisfaction with life scale (SWLS) and wrote self-statements about their current life situation. The LLM-based ML model, using a LightGBM regressor, achieved a correlation of 0.542 with self-reported SWLS scores, outperforming both the standalone LLM ( r = 0.491) and expert ratings ( r = 0.455). Effect size analysis revealed a statistically significant moderate effect size difference between the LLM-based ML model and expert ratings (Cohen’s d = 0.499, 95% CI [0.043, 0.955]). These findings demonstrate the potential of integrating LLM and ML for an efficient and accurate assessment of life satisfaction, challenging conventional methods, and opening new avenues for psychological measurement. The study’s implications extend to research, clinical practice, and policymaking, offering promising advancements in AI-assisted psychological assessment.