Abstract

Monitoring how different people – as ‘social sensors’ – evaluate and respond to crisis such as pandemics, allows tailoring crisis communication to the social perceptions of the situation, at different moments. To gather such evidence, we proposed a index of social perceptions of systemic risk (SPSR), as an indicator of a situational threat compromising risks to physical health, psychological health, the economy, social relations, health system, and others. This indicator was the core of a social sensing approach applied to crisis situations, implemented during the COVID-19 pandemic through a content analysis of more than 130.000 public comments from Facebook™ users, in COVID-19 related publications. This content coding allowed creating a SPSR index monitored during a one-year descriptive longitudinal analysis. This index correlated with co-occurring events within the social system, namely epidemiological indicators across measurement cycles (e.g. new deaths; cumulative number of infection cases; Intensive Care Unit hospitalizations) and tended to reflect the epidemiological situation severity (e.g. with the highest level registered during the worst pandemic wave). However, discrepancies also occurred, with high SPSR registered in a low severity situation, i.e. low number of hospitalizations and deaths (e.g. school year beginning), or low SPSR in a high severity situation (e.g. 2nd pandemic wave during Christmas), showing other factors beyond the epidemiological situation contributing to the social perceptions. After each ‘crisis period’ with SPSR peaking, there was a ‘restoration period’, consistently decreasing towards average levels of the previous measurement cycle. This can either indicate social resilience (recovery and resources potentiation) or risk attenuation after a high-severity period. This study serves as preliminary proof of concept of a crises social sensing approach, enabling monitoring of social system dynamics for various crisis types, such as health crisis or the climate crisis.

Full Text
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