Analyzing social media with crowdsourcing in Crowd4SDG
This provides a flexible solution for data analysts and emergency responders to analyze social media more efficiently, though it is incremental as it builds on existing methods.
The study tackled the challenge of extracting relevant information from social media during emergencies by developing an approach that combines automatic data processing tools with human-in-the-loop crowdsourcing, validated through three case studies in the Crowd4SDG project.
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.