GESIS ›Meet the Experts‹
Peoples› activities and opinions recorded as digital traces online, especially on social media and other web-based platforms, offer increasingly informative pictures of the public. They promise to allow inferences about populations beyond the users of the platforms on which the traces are recorded, representing real potential for the Social Sciences and a complement to survey-based research. But the use of digital traces brings its own complexities and new error sources to the research enterprise. Recently, researchers have begun to discuss the errors that can occur when digital traces are used to learn about humans and social phenomena.
This talk discusses various strategies for critical reflection on the limitations, implications, and consequences of using digital traces for measuring social constructs. Inspired by the Total Survey Error (TSE) Framework developed for survey methodology, we introduce a conceptual framework to diagnose, understand, and document errors that may occur in studies based on such digital traces. While there are clear parallels to the well-known error sources in the TSE framework, the new ›Total Error Framework for Digital Traces of Human Behavior on Online Platforms‹ (TED-On) identifies several types of error that are specific to the use of digital traces. By providing a standard vocabulary to describe these errors, the proposed framework and this talk advances communication and research concerning the use of digital traces in scientific social research.
Dr. Fabian Flöck leads the Data Science team at the Computational Social Science Department at GESIS. He is interested in the validity and transparency of automated measurement in social science contexts, but also researches interactive data analysis services, collaborative content creation and digital communication processes. He studied communication sciences and sociology, and subsequently acquired a PhD in computer science.
Indira Sen is a doctoral candidate at GESIS, working at the intersection of Computational Social Science and Natural Language Processing. She has a bachelor‹s and master's degree in Computer Science, and currently works on measuring social constructs like political attitudes and hate speech from social media data and understanding the limitations inherent to this task.