Analyzing social media with crowdsourcing in Crowd4SDG

• URL: http://arxiv.org/abs/2208.02689v1
• Date: Thu, 4 Aug 2022 14:42:20 GMT
• Title: Analyzing social media with crowdsourcing in Crowd4SDG
• Authors: Carlo Bono, Mehmet O\u{g}uz M\"ul\^ay\.im, Cinzia Cappiello, Mark Carman, Jesus Cerquides, Jose Luis Fernandez-Marquez, Rosy Mondardini, Edoardo Ramalli, and Barbara Pernici
• Abstract summary: This study presents an approach that provides flexible support for analyzing social media, particularly during emergencies. 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. Such support includes both feedback and suggestions to configure automated tools, and crowdsourcing to gather inputs from citizens.
• Score: 1.1403672224109254
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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