Analyzing social media with crowdsourcing in Crowd4SDG

TL;DR

This paper presents a flexible social media analysis approach during emergencies, combining automated tools and crowdsourcing to improve information retrieval from large, diverse posts, validated through three case studies.

Contribution

It introduces a human-in-the-loop framework integrating automation and crowdsourcing for social media analysis in emergency contexts, with practical validation.

Findings

Effective filtering, classification, and geolocation of social media content.

Enhanced data analysis support through human-in-the-loop and crowdsourcing.

Validated approach with three real-world case studies.

Abstract

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.