  Social Media provides a trove of information that, if aggregated and analysed
appropriately can provide important statistical indicators to policy makers. In
some situations these indicators are not available through other mechanisms.
For example, given the ongoing COVID-19 outbreak, it is essential for
governments to have access to reliable data on policy-adherence with regards to
mask wearing, social distancing, and other hard-to-measure quantities. In this
paper we investigate whether it is possible to obtain such data by aggregating
information from images posted to social media. The paper presents VisualCit, a
pipeline for image-based social sensing combining recent advances in image
recognition technology with geocoding and crowdsourcing techniques. Our aim is
to discover in which countries, and to what extent, people are following
COVID-19 related policy directives. We compared the results with the indicators
produced within the CovidDataHub behavior tracker initiative. Preliminary
results shows that social media images can produce reliable indicators for
policy makers.
