Analyzing social media with crowdsourcing in Crowd4SDG
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 50 references that are in the map. First posted on arXiv 4 Aug 2022.
Builds on
9 of 50 references.
References · 9
- 2014Processing Social Media Messages in Mass Emergency: A Surveycited by 50
- 2018CrowdTruth 2.0: Quality Metrics for Crowdsourcing with Disagreementcited by 27
- 2017Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crisescited by 8
- 2019Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approachcited by 5
- 2022TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a comparison with alternative sourcescited by 2
- 2019Citizen Science: An Information Quality Research Frontiercited by 1
- 2020Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twittercited by 1
- 2021A first approach to closeness distributionscited by 1
- 2022A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligencecited by 1
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