Abstract
AbstractCrowdsource volunteering efforts have contributed significantly to pandemic response and recovery during the COVID-19 outbreak. In such efforts, individual volunteers can collaborate to achieve rapid mobilisation toward emergent community demands. In this study, we quantitively study this phenomenon using the concept of self-organisation, by proposing a data-driven framework to investigate when and how self-organisation emerged during the pandemic response and how it relates to effectiveness of volunteer organisations in general. Using activity data collected from a mobile volunteer platform in Shenzhen, China, we found that volunteers’ task participation and social and task preferences show multiple phases of self-organisation in response to changing epidemic situations and centralised interventions. Simulation experiments further show that the self-organised volunteer system can become more responsive and more robust in the face of uncertain community demands with minimal centralised guidance.
Publisher
Springer Science and Business Media LLC
Subject
General Economics, Econometrics and Finance,General Psychology,General Social Sciences,General Arts and Humanities,General Business, Management and Accounting
Cited by
8 articles.
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1. Volunteer Future Collaboration Prediction with Topology-Based Ensemble Models;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17
2. An Ensemble Learning Method for Volunteer Retention Prediction;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17
3. Volunteer Retention and Future Collaboration Prediction in Volunteer Crowdsourcing Platforms;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17
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5. An Ensemble Link Prediction Framework With AUC-Guided Leaderboard Probing For Volunteer Collaboration Prediction Challenge;2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP);2023-09-17