RESUMO
The aim of this study was to investigate trends in suicide rates (SRs) among the elderly in China. Annual data on SRs among Chinese people ≥ the age of 65 were collected from China's Health Statistics Yearbook from 2002 to 2020. Then, data were stratified by age, region, and sex. Standardized SRs were calculated and analyzed using a conventional joinpoint regression model. Results revealed that overall, SRs among the elderly in China tended to decline from 2002-2020. Fluctuations in SRs, including in 2004-2005 due to the SARS epidemic, in 2009-2010 due to the economic crisis, and in 2019-2020 due to the COVID-19 pandemic, were also observed. Data suggested a relatively greater crude SR among the elderly (vs. young people), in males (vs. females), and in people living in a rural area (vs. those living in an urban area). SRs tended to rise with age. Joinpoint regression analysis identified joinpoints only for males ages 65-69 and over the age of 85 living in a rural area, suggesting that individuals in these groups are more sensitive to negative stimuli and more likely to commit suicide, necessitating closer attention. The findings from this study should help to make policy and devise measures against suicide in the future.
RESUMO
During the COVID-19 pandemic, when individuals were confronted with social distancing, social media served as a significant platform for expressing feelings and seeking emotional support. However, a group of automated actors known as social bots have been found to coexist with human users in discussions regarding the coronavirus crisis, which may pose threats to public health. To figure out how these actors distorted public opinion and sentiment expressions in the outbreak, this study selected three critical timepoints in the development of the pandemic and conducted a topic-based sentiment analysis for bot-generated and human-generated tweets. The findings show that suspected social bots contributed to as much as 9.27% of COVID-19 discussions on Twitter. Social bots and humans shared a similar trend on sentiment polarity-positive or negative-for almost all topics. For the most negative topics, social bots were even more negative than humans. Their sentiment expressions were weaker than those of humans for most topics, except for COVID-19 in the US and the healthcare system. In most cases, social bots were more likely to actively amplify humans' emotions, rather than to trigger humans' amplification. In discussions of COVID-19 in the US, social bots managed to trigger bot-to-human anger transmission. Although these automated accounts expressed more sadness towards health risks, they failed to pass sadness to humans.