Computational methods challenge conventional visual analysis tools, inviting to a deeper understanding of uses of visual images in massive volumes by audiences themselves. Audiences have always used found images or amateur photographs, but such practices intensified with the advent of Web 2.0 and social media. More and more they are understood as a kind of self-motivated, amateur curatorial work. In this paper we explore the cultural roots and emotional motivation behind image selection by mass audiences in social media. We take an instance in the COVID-19 pandemic as a case study: images used in Twitter the day Omicron variant was announced. Using computational methods, we collected and analysed approx.23,000 images. We found a divide between textual and pictorial images, which may be explained based on cultural grounds. We also found that different kinds of imagery were related to tweets expressing differences in certain emotions like Fear or Sadness, and on secondary emotions like Despair, Guilt and Aggressiveness. The findings show that the connection between the visual, the textual, and the emotional elements is a fruitful field for novel knowledge.