The emergence of COVID-19 and its associated containment strategies, such as lockdowns and social distancing, are expected to impact mental health, which could be more severe among people with preexisting mental health disorders. In this research, we aim to better understand the changes in mental health during the COVID-19 pandemic by analysing data from mental health-related communities. We have collected data from the 15th of February 2020 to the 15th of July 2020 and analysed these data using interaction, linguistic structure, and interpersonal awareness measures. Our findings show that early in the lockdown, individuals showed selflessness, solidarity, and low rates of seeking help, but they also showed a negative mental health state. Moreover, considering the importance of social support in mental illness, we also aim to explore what derives social support in mental health communities. We found that receiving high social support was hindered by the use more swearing and negative emotional or self-referent words. Furthermore, not receiving social support may push actual help seekers to repeat their posts, which may be considered spamming in Reddit forums. Hence, we investigated the characteristics of duplicate posts authored by real help seekers to build a spam classifier. Our investigation showed that actual help seekers tend to show different levels of mental health when they repeat their posts for seeking immediate help.
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Althobaity, S. & Jose, J. 2022, 'Mental health issues during COVID-19: A data exploration', Social Informatics: 13th International Conference, SocInfo 2022, Glasgow, UK, October 19–21, 2022, Proceedings, Cham, Switzerland. https://eprints.gla.ac.uk/281151/