Computational Social Science Approaches in Sustainability and Ecological Crisis Research

Dear Colleagues,

Computational Social Science is a relatively new, but expanding and exciting field that accommodates computer scientists, physicists, mathematicians, social scientists, economists, etc. who are developing and applying advanced computational and mathematical methods such as large online experiments, computer simulations, big data analysis, machine learning, etc. to address a wide spectrum of social science research questions. In this Special Issue we want to focus on what contribution this new exciting field of computational social science can make to sustainability research and the research of ecological crises. Contributions are welcome that focus on the following topics (but not limited to these):

1. How social media data can be used in assessing and responding to ecological catastrophes;
2. How agent-based models (ABMs) and other simulation models can be used to investigate various sustainability issues and ecological crises;
3. How social media data can be used to understand people’s perceptions of and responses to the climate and other ecological crises;
4. How social media and other online data can be used to highlight and better understand the links between ecological and other societal, political, and economic crises;
5. How (large) online experiments or mobile-phone-based field experiments can be used to test interventions to address various environmental (behavioral) problems;
6. How large event databases such as GDELT can be used to study ecological crises;
7. How machine learning can be applied in sustainability and ecological crisis research.

Prof. Dr. Viktoria Spaiser
Dr. Shyam Ranganathan
Guest Editors

Manuscript Submission Information

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• computational social science
• sustainability
• ecological crisis
• climate crisis
• agent-based models (ABMs)
• big data
• online experiment
• machine learning
• digital methods
• data science