# Computational Social Science of Disasters: Opportunities and Challenges
## 5 Computational Social Science of Disasters
### Challenges and Opportunities  
The field of CSSD encompasses a cycle of interactions in data analysis, computational models, and social theories in the scientific process, and as discussed above, we find examples of this in the literature. In reviewing the work of these areas, we hope it has become clear to the reader that, while each element of CSSD is present, the full conceptualization of CSSD has not been achieved. Two examples of this are data not being used to inform models and few applications of disaster theory in agent-based models. More critically, there is no significant published disaster research that completes a full cycle of interactions in data analysis, computational models, and social theories. Future work needs to close these gaps to take advantage of the opportunities and address the challenges in this new field of study. As with any emerging field, there are many challenges ranging across a wide spectrum of topical, technical, methodological, and ethical issues. Topically, we see an overreliance on case-study analysis in agent-based models, the disaster sciences, and crisis informatics research, caused in part by the context-dependent nature of disasters and the challenge of sharing data and models. Open science practices can support open exchange of research and allow for generalization to larger theory and replication with platforms sharing data (e.g., Dataverse project [247]) and models (e.g., OpenABM, the Computational Modeling in Social and Ecological Sciences (COMSES, [248]), and GitHub [249]). The nature of most new sources of data (e.g., public polls and social media) is short-term and post-event, and they contribute to understanding the processes of preparedness and response in disaster. CSSD needs to develop strategies for obtaining longitudinal sources of data for the long-term processes evident in mitigation and recovery stages of disaster. From a technical and methodological perspective, challenges in the forms of data, the collection techniques, and the machine learning algorithms all create biases in the data (as discussed in Section 3.1 and [250]). Verification and validation are also problematic. For example, deep learning algorithms suffer from a lack of human interpretability because their machine learning processes operate in a black box and do not have intermediate measures to verify whether they are performing as intended. Validation (demonstrating that results align with real-world outcomes) in this area also suffers from a number of issues, including a lack of high-quality, real-world datasets for comparison to model outputs. The complex subject matter of disasters presents a challenge with the requirement of analyzing heterogeneous variables and multiple interacting processes that prevent the isolation and evaluation of specific actors and processes. The greatest opportunity in CSSD is the wealth of data sources now available to researchers. Big data and ICT are providing new data sources in the forms of online data collection, social media, and VGI. These sources enable the quick mapping of roads and geographic terrain of disaster event areas, individual reports of events on the ground, and more sophisticated online data collection applications and organizations, such as Ushahidi [161], Missing Maps [251], and Humanitarian OpenStreetMap Team (HOT [252]), that can now be implemented during disaster events. These data opportunities can be expanded with decision support science for improved decision-making in agent-based models such those used for wildfire training, incident command, and community outreach [253]. For instance, SimTable was used in the 2016 Sand Fire in California [254]. Not only are these platforms being used to inform policy decision-making on aid, but they also provide easier post-event data collection using the footprints of digital activities. Because the collection of disaster information can be undertaken post-event and far from the event's location, researchers can help address a major limitation in disaster research, \"unobservability\" [7].