Causal discovery in social media using quasi-experimental designs

Abstract

Social media systems have become increasingly attractive to both users and companies providing those systems. Efficient management of these systems is essential and requires knowledge of cause-and-effect relationships within the system. Online experimentation can be used to discover causal knowledge; however, this ignores the observational data that is already being collected for operational purposes. Quasi-experimental designs (QEDs) are commonly used in social sciences to discover causal knowledge from observational data, and QEDs can be exploited to discover causal knowledge about social media systems. In this paper, we apply three different QEDs to demonstrate how one can gain a causal understanding of a social media system. The conclusions drawn from using a QED can have threats to their validity, but we show how one can carefully construct sophisticated designs to overcome some of those threats.