This dataset contains the VisualCit data for social distance and face masks derived from social media image analysis. From Twitter crawls with COVID-10 keywords, images are filtered ewith ML classifiers in order to retrieve images of people in public places which are photos. With crowdsourcing additional information is added about COVID-19 related behavioral aspects, with the goal of deriving indicators for decision makers to assess the ongoing situation. In this analysis we focus on the percentages of people wearing masks and maintaining social distances. Reference paper: V. Negri, D. Scuratti, S. Agresti, D. Rooein, G. Scalia, J.L. Fernandez Marquez, A. Ravi Shankar, M. Carman and B. Pernici, Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter, ICSE - Track Software Engineering in Society, May 2021 https://arxiv.org/abs/2010.03021 Abstract 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.

{"references": ["V. Negri, D. Scuratti, S. Agresti, D. Rooein, G. Scalia, J.L. Fernandez-Marquez, A. Ravi Shankar, M. Carman and B.Pernici, Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter, ICSE - Track Software Engineering in Society, May 2021 http://hdl.handle.net/11311/1161146"]}

The metadata for the files in this dataset are described in the file crowd4SDG-VisualCit-COVID-19-metadata.docx in the dataset below.