The psychological effects of quarantine during COVID-19 outbreak: Sentiment analysis of social media data
Lu, W.; Yuan, L.; Xu, J.; Xue, F.; Zhao, B.; Webster, C.
Show abstract
We rely on social distancing measures such as quarantine and isolation to contain the COVID-19. However, the negative psychological effects of these measures are non-negligible. To supplement previous research on psychological effects after quarantine, this research will investigate the effects of quarantine amid COVID-19. We adopt a sentiment analysis approach to analyze the psychological state changes of 1,278 quarantined persons 214,874 tweets over four weeks spanning the period before, during, and after quarantine. We formed a control group of 1,278 unquarantined persons with 250,198 tweets. The tweets of both groups are analyzed by matching with a lexicon to measure the anxious depression level changes over time. We discovered a clear pattern of psychological changes for quarantined persons. Anxious depression levels significantly increased as quarantine starts, but gradually diminished as it progresses. However, anxious depression levels resurged after 14 days quarantine. It was found that quarantine has a negative impact on mental health of quarantined and unquarantined people. Whilst quarantine is deemed necessary, proper interventions such as emotion management should be introduced to mitigate its adverse psychological impacts.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Understanding mental health trends during COVID-19 pandemic in the United States using network analysis 94%
- Misinformation on covid-19 origin and its relationship with perception and knowledge about social distancing: A cross-sectional study 93%
- What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media 93%
Similar papers in this journal
- Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter using machine learning 94%
- Users’ Reactions on Announced Vaccines against COVID-19 Before Marketing in France: Analysis of Twitter posts 93%
- Fear of Infection and Sufficient Vaccine Reservation Information Might Drive Rapid Coronavirus Disease 2019 Vaccination in Japan: Evidence from Twitter Analysis 93%
Similar papers in this journal
- The psychological impact of 'mild lockdown' in Japan during the COVID-19 pandemic: a nationwide survey under a declared state of emergency 93%
- The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis 93%
- The experience of distress during the COVID-19 outbreak: a cross-country examination on the fear of COVID-19 and the sense of loneliness 92%
Similar papers in this journal
- Yet another lockdown? A large-scale study on people’s unwillingness to be confined during the first 5 months of the COVID-19 pandemic in Spain 94%
- Social distance and SARS memory: impact on the public awareness of 2019 novel coronavirus (COVID-19) outbreak 92%
- Comparing protein-protein interaction networks of SARS-CoV-2 and (H1N1) influenza using topological features 91%
Similar papers in this journal
- Impact on Mental Health of students due to restriction caused by COVID-19 pandemic: Cross-sectional study 93%
- Predicting COVID-19 Pandemic in Saudi Arabia Using Modified Singular Spectrum Analysis 90%
- Lessons learned from the resilience of Chinese hospitals to the COVID-19 pandemic: a scoping review 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.