Crowd4SDG-VisualCit COVID-19 behavioral indicators

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.本数据集包含源自社交媒体图像分析的、用于社交距离与口罩佩戴研究的VisualCit（VisualCit）数据。 本数据集通过抓取携带COVID-19关键词的推特（Twitter）内容获取图像，随后利用机器学习（Machine Learning, ML）分类器对图像进行筛选，以提取公共场所内的人物实拍照片。此外，通过众包（Crowdsourcing）方式补充与COVID-19相关的行为层面信息，旨在为决策者提供评估当前疫情态势的量化指标。本次分析聚焦于人群口罩佩戴率与社交距离保持率两项指标。 参考文献： V. Negri、D. Scuratti、S. Agresti、D. Rooein、G. Scalia、J.L. Fernandez Marquez、A. Ravi Shankar、M. Carman 与 B. Pernici，《基于图像的社会感知：结合人工智能与众包技术从推特中挖掘政策合规指标》，ICSE - 软件工程与社会轨道，2021年5月，https://arxiv.org/abs/2010.03021 摘要 社交媒体蕴藏着丰富的信息资源，若能对其进行合理聚合与分析，可为政策制定者提供重要的统计指标。在部分场景下，这类指标无法通过其他渠道获取。例如，在当前COVID-19疫情大流行期间，各国政府亟需获取关于口罩佩戴、社交距离等难以量化的政策合规性可靠数据。本文探讨了通过聚合社交媒体上传的图像信息来获取此类数据的可行性。文中提出了VisualCit（VisualCit）框架，这是一种基于图像的社会感知流水线，结合了当前图像识别技术的最新进展、地理编码（Geocoding）与众包（Crowdsourcing）技术。我们的目标是探究全球哪些国家、以及在多大程度上，民众遵守了与COVID-19相关的政策指令。 我们将研究结果与CovidDataHub行为追踪计划所生成的指标进行了对比。初步结果表明，社交媒体图像可为政策制定者提供可靠的量化指标。