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.

With the recent advances in photobiology research and light-emitting diode technology, lighting considering circadian effects and the potential health benefits attract much attention. In this study, we demonstrate that the common practice of spectral optimisation of light for high visual efficacy can potentially lead to very inefficient delivery of circadian stimulus, which contributes to the lack of circadian entrainment that is likely to happen in indoor environments with only electric lighting. To optimise spectra of white light-emitting diodes for circadian efficacy, a four-component colour-mixing method with explicit analytical solutions is introduced. Energy-saving up to 29% is achieved at a target circadian stimulus of 0.35, by switching from the traditional maximum-visual-efficacy strategy to a maximum-circadian-efficacy strategy. Moreover, we propose a framework of a novel lighting-design space which allows practitioners to explore the possible combinations of circadian stimulus, visual illuminance and colour temperature. Solutions are provided for scenarios where activation of the circadian system should be avoided while a reasonable visual brightness appearance is maintained.

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