Special Issue in Social Science Computer Review
Conceptualizing, Assessing, and Improving the Quality of Digital Behavioral Data
1GESIS – Leibniz Institute for the Social Science, 2University of Eichstätt-Ingolstadt, 3Leipzig University, 4RWTH Aachen University, 5Complexity Science Hub Vienna
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. As a consequence, social actors using these technologies leave a multitude of digital traces in many areas of life that sum up to an enormous amount of data about human behavior and attitudes. This new data type, referred to as “digital behavioral data” (DBD), is extremely diverse, ranging from Twitter texts to life tracking data generated by wearables such as fitness trackers. Furthermore, these data often are high in volume and velocity, offer high temporal and spatial granularity, and rich relational information – e.g., in the form of large social network structures.
In recent years, the social sciences have increasingly recognized the potential of DBD to address new substantive research questions in many research fields. However, the scientific use of DBD is also associated with a number of entirely new challenges. In contrast to survey data, the data type still predominant in the social sciences, most DBD are not generated for research purposes but are a side product of our daily activities. Hence, the process of data generation is not based on elaborate research designs, which in turn may have serious 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.
For this reason, this special issue focuses on (i) the conceptualization of DBD quality, methodological innovations for its (ii) assessment, and (iii) improvement as well as their sophisticated empirical application. We invite, among others, high-quality submissions on the following topics:
- Development of quality standards for DBD and DBD-based measurements
- Case studies on the quality of DBD
- Conceptional data quality models/frameworks for DBD and their application
- Quantification of error components related to DBD
- Mitigation strategies to improve the quality of DBD based research (e.g., new research designs, documentation strategies, statistical/computational methods)
- Big data as a cure or curse for limitations in data quality
- Challenges in terms of reproducibility and open science (e.g., DBD documentation, DBD sharing, transparency, and reusability of DBD-based measurements)
- Ethics of DBD-based measurements and frameworks to anticipate and mitigate harmful consequences of DBD-based measurements
- None so far
- March 21, 2022: Call issued
- June 5, 2022: Extended abstracts due
- June 22, 2022: Authors receive feedback and invitation to submit a full paper
- January 31, 2023: Submission of full paper
- Fall 2023: Expected publication date
The Special Issue will apply a two-step reviewing process. First, the submission of an extended abstract (maximum of 1,000 words) is required. These extended abstracts are mandatory and serve as a basis for the editors to decide on the invitation to submit full papers. Second, invited authors submit a full paper which will be subject to peer-review with at least two experts in the field. Papers with a final acceptance are expected to be published online in late 2023.
- Extended abstracts (maximum of 1,000 words) are to be submitted to
- Completed papers may not exceed 10,000 words, including endnotes and bibliographical references, and must conform to the SSCR formatting guidelines, accessible at https://journals.sagepub.com/author-instructions/SSC.
Contact and questions
Please send any questions to dbd-si-sscr(at)gesis(dot)org.