# Computational Social Science of Disasters: Opportunities and Challenges
## 3 Computational Social Science
### Information Retrieval and Open Data Systems  
Advances in processing technologies have made automated information retrieval standard practice in the social sciences, and these technologies can be used to detect social, behavioral, or economic patterns. In this area, information extraction algorithms are used to collect data from disparate sources, such as census records, economic data, newspapers, and social media, and to conduct data mining and content analysis of verbal data, such as interviews, speeches, and legislative testimony [19; 105]. Information retrieval was traditionally defined as \"finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers) [106]\". Computational social scientists engage in this activity by collecting and analyzing any digital traces that potentially address their social science inquiries (such as elections and international relations, e.g., [107; 108; 109; 110]). There are technical challenges in this realm that include evaluation of item similarities, data scalability, and time sensitivity [111]. [PERSON] [112] comprehensively explores the characteristics of digital data, including its strengths (voluminous, always-on, and non-reactive) and its weaknesses (incomplete, inaccessible, non-representative, drifting, algorithmically confounded, dirty, and sensitive).  
To leverage new capabilities in information retrieval, many governments and companies are adopting open data policies that allow researchers to access and study these social data. Prominent examples of such initiatives include Data.gov in the U.S. and OpenKenya [113] in the Republic of Kenya (see [114] for how such data can be used). The ever-increasing popularity of social media, enabled by Web 2.0 technology, is expanding the sources and volume of social data relevant to our daily lives through applications such as Facebook, Twitter, and Instagram, and these open sources are allowing researchers to explore a vast range of topics, including opinions during elections [115], opinions on public health [116], data on disease outbreaks [117], and studies of the connections between people and places [118; 119]. The relevance and management of open-source data has become more important than ever, and they are well-positioned to support the quantitative study of disasters through the use of new computational methods, such as machine learning, natural language processing, sentiment analysis, and artificial intelligence [120].  
Large-scale social data harvested from a variety of sources can be classified as part of a general category of \"observational social data,\" and these data vary depending on researchers' interests and approaches. Their uses include identification of characteristics or patterns by quantitative and qualitative descriptions of individuals or groups, development of macro-level mathematical models of dynamics in data aggregates, identification of statistical relationships between variables and outcomes, examination of the emergent patterns on the aggregate level, calibration of parameters in computational simulations, inference of social events, and forecasting social phenomena (see [117; 121; 122; 123; 124] for a range of uses of such large-scale data).