Conceptualizing, Assessing, and Improving the Quality of Digital Behavioral Data

Abstract

The spread of modern digital technologies, such as social media online platforms, digital marketplaces, smartphones, and wearables, is increasingly shifting social, political, economic, cultural, and physiological processes into the digital space. Social actors using these technologies (directly and indirectly) leave a multitude of digital traces in many areas of life that sum up an enormous amount of data about human behavior and attitudes. This new data type, which we refer to as “digital behavioral data” (DBD), encompasses digital observations of human and algorithmic behavior, which are, amongst others, recorded by online platforms (e.g., Google, Facebook, or the World Wide Web) or sensors (e.g., smartphones, RFID sensors, satellites, or street view cameras). However, studying these social phenomena requires data that meets specific quality standards. While data quality frameworks—such as the Total Survey Error framework—have a long-standing tradition survey research, the scientific use of DBD introduces several entirely new challenges related to data quality. For example, most DBD are not generated for research purposes but are a side product of our daily activities. Hence, the data generation process is not based on elaborate research designs, which in turn may have profound implications for the validity of the conclusions drawn from the analysis of DBD. Furthermore, many forms of DBD lack well-established data models, measurement (error) theories, quality standards, and evaluation criteria. Therefore, this special issue addresses (i) the conceptualization of DBD quality, methodological innovations for its (ii) assessment, and (iii) improvement as well as their sophisticated empirical application.