Political Science and Big Data: Structured Data, Unstructured Data, and How to Use Them

How do institutional dimensions of open government data affect innovation? Evidence from research institutes in China

“…However, other scholars point out the problems of highly structured data. Grossman and Pedahzur (2020) argue that in the big data era, only 15-20% of existing data are structured data, while most available data are unstructured, including political speeches, pictures, video recordings, media broadcastings, policy/regulatory documents and massive blog posts generated by the wider public. And these unstructured data are growing much faster than structured data (Zikopoulos et al, 2012).…”

“…And these unstructured data are growing much faster than structured data (Zikopoulos et al, 2012). For these data, governments are not advised to open them in a highly structured format, since data structuring process is a reductive process that inevitably entails the loss of details and context, and the structuring process may not keep the pace of data generation (Grossman and Pedahzur, 2020). These data are qualitative in nature and human-readable.…”

“…This finding is in line with what we argued in the theoretical framework: governments can publish both quantitative, machine-readable datasets in rows and columns and qualitative, human-readable texts, audios and videos. The reason is that structured and unstructured data have their respective advantages: structured data that is organized into a predefined consistent format is easier to store, search, retrieve and analyze and thus allows for efficient data processing and simplified data management; unstructured data can provide richer and diverse information, especially valuable contextual information and can offer researchers a more comprehensive and holistic view of the data and allowing for deeper analysis and understanding (Grossman and Pedahzur, 2020).…”

Can We Algorithmize Politics? The Promise and Perils of Computerized Text Analysis in Political Research

“…The eminent spread of digital interactions, social networks, and online activities that have reshaped our social habitat is encouraging researchers across disciplines to rethink and revise the main paradigmatic frameworks of social and political research (Jungherr and Theocharis 2017, 99; Lazer et al 2009, 722. Indeed, in recent years, political scientists have used digital datafication trends (Mayer-Schonberger and Cukier 2014) to introduce new types of data and compile an incredible array of new databases (Grossman and Pedahzur 2020, 226). Computational social sciences harness the use and spread of big data and machine-learning tools for modeling, simulating, and scrutinizing social phenomena by computational means (Brady 2019, 297–98).…”