Social media analysis and crowd-sourcing for disaster management

Increase in access to mobile phone devices and social media networks has changed the way people report and respond to disasters. Community-driven initiatives such as Stand By Task Force (SBTF) or GISCorps have shown great potential by crowdsourcing the acquisition, analysis, and geolocation of social media data for disaster responders. To make social media information suitable for emergency responders, these initiatives face two main challenges: (1) Most of social media content such as photos and videos are not geolocated, thus preventing the information to be used by emergency responders,  and (2) they lack tools to manage volunteers' contributions and aggregate them in order to ensure high quality and reliable results.
 This seminar illustrates Crowd4EMS a crowdsourcing platform developed under the EU project E2mC: Evolution of Emergency Copernicus services. Crowd4EMS combines automatic methods for gathering information from social media and crowdsourcing techniques in order to manage, aggregate volunteers' contributions, and ensure reliable for emergency responders in disaster management.

Dr. Jose Luis Fernandez-Marquez (Male) is Senior Lecturer at the University of Geneva (UNIGE), and  head of the Geneva-Tsinghua Initiative Accelerator.  He has a computer science background, PhD in collective artificial intelligence, and wide experience in Citizen Science.  In 2011 he joint UNIGE after his PhD defence at the Artificial Intelligence Research Institute (IIIA-CSIC).  In 2014, he formally joint the Citizen Cyberlab a partnership between UNIGE, CERN, and the United Nation for Training and Research (UNITAR) aiming at encouraging citizens and scientists  to collaborate in new ways to solve big challenges. Since 2019, he is technical coordinator of the Crowd4SDG EU project which focuses on demonstrating the potential of Citizen Science for monitoring and achieving the SDGs.

His current research focus on citizen science data quality analysis and methodologies to make citizen science data  suitable for decision/policy makers.