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

Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter