论文标题
基于图像的社交感应：将AI和人群与Twitter的政策遵守指标相结合
Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter
论文作者
论文摘要
社交媒体提供了一系列信息，如果汇总和分析适当地可以为政策制定者提供重要的统计指标。在某些情况下，这些指标无法通过其他机制获得。例如，鉴于持续的COVID-19爆发，政府必须访问有关掩盖，社交疏远和其他难以估量的数量的可靠数据。在本文中，我们调查是否可以通过从发布到社交媒体的图像中汇总信息来获取此类数据。该论文介绍了远导，这是一种基于图像的社会感测的管道，将图像识别技术的最新进展与地理编码和众包技术结合在一起。我们的目的是在哪些国家以及人们遵循Covid-19相关政策指令的范围内发现。我们将结果与Coviddatahub行为跟踪器计划中产生的指标进行了比较。初步结果表明，社交媒体图像可以为政策制定者产生可靠的指标。
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.
