During the COVID-19 pandemic, every day, updated case numbers and the lasting time of the pandemic became major concerns of people. We collected the online data (28 January to 7 March 2020 during the COVID-19 outbreak) of 16,453 social media users living in mainland China. Computerized machine learning models were developed to estimate their daily scores of the nine dimensions of the Symptom Checklist—90 (SCL-90). Repeated measures analysis of variance (ANOVA) was used to compare the SCL-90 dimension scores between Wuhan and non-Wuhan residents. Fixed effect models were used to analyze the relation of the estimated SCL-90 scores with the daily reported cumulative case numbers and lasting time of the epidemic among Wuhan and non-Wuhan users. In non-Wuhan users, the estimated scores for all the SCL-90 dimensions significantly increased with the lasting time of the epidemic and the accumulation of cases, except for the interpersonal sensitivity dimension. In Wuhan users, although the estimated scores for all nine SCL-90 dimensions significantly increased with the cumulative case numbers, the magnitude of the changes was generally smaller than that in non-Wuhan users. The mental health of Chinese Weibo users was affected by the daily updated information on case numbers and the lasting time of the COVID-19 outbreak.

This manuscript provides a comprehensive analysis of the discrepancies in China’s reported and estimated crude birth rates (CBR) over the thirty years from 1990 to 2020, revealing substantial underreporting influenced by family planning policies and demographic shifts. Utilizing data from National Population Censuses and multiple national surveys, this study highlights how birth numbers, crude birth rates, and fertility rates have been consistently adjusted to align with policy narratives rather than actual demographic data. The findings indicate significant differences between direct calculations, reported figures, subsequent post-census adjustments, and estimations for the reality, reflecting a complex interplay of governmental data management and policy enforcement. By reconstructing the thirty-year trajectory of CBR and fertility in China, this paper offers critical insights into the implications of data accuracy for policy making and demographic analysis. The study concludes that the lack of accurate data over the thirty years and official misjudgments of fertility levels are the most important reasons for the failure to liberalize the fertility policy as early as possible.