Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data analysis project usually requires time and technical skills. This study presents an approach that provides flexible support for analyzing social media, particularly during emergencies. Different use cases in which social media analysis can be adopted are introduced, and the challenges of retrieving information from large sets of posts are discussed. The focus is on analyzing images and text contained in social media posts and a set of automatic data processing tools for filtering, classification, and geolocation of content with a human-in-the-loop approach to support the data analyst. Such support includes both feedback and suggestions to configure automated tools, and crowdsourcing to gather inputs from citizens. The results are validated by discussing three case studies developed within the Crowd4SDG H2020 European project.
翻译:社会媒体有可能及时提供有关紧急情况和突发事件的信息,然而,在每天张贴的数以百万计的职位中找到相关信息可能很困难,而开发数据分析项目通常需要时间和技能。本研究报告提出一种为分析社会媒体提供灵活支持的方法,特别是在紧急情况下。引入社会媒体分析的不同使用案例,讨论从大量职位上检索信息的挑战。重点是分析社交媒体文章中包含的图像和文本,以及一套自动数据处理工具,用于过滤、分类和地理定位内容,并采用 " 人到流 " 方法支持数据分析员。这种支持包括反馈和建议,以配置自动工具,以及众包收集公民投入。通过讨论Crowd4SDG H2020欧洲项目开发的三项案例研究,验证了这些结果。