Quantifying participation biases on social media

AbstractAround seven-in-ten Americans use social media (SM) to connect and engage, making these platforms excellent sources of information to understand human behavior and other problems relevant to social sciences. While the presence of a behavior can be detected, it is unclear who or under what circumstances the behavior was generated. Despite the large sample sizes of SM datasets, they almost always come with significant biases, some of which have been studied before. Here, we hypothesize the presence of a largely unrecognized form of bias on SM platforms, called participation bias, that is distinct from selection bias. It is defined as the skew in the demographics of the participants who opt-in to discussions of the topic, compared to the demographics of the underlying SM platform. To infer the participant’s demographics, we propose a novel generative probabilistic framework that links surveys and SM data at the granularity of demographic subgroups (and not individuals). Our method is distinct from existing approaches that elicit such information at the individual level using their profile name, images, and other metadata, thus infringing upon their privacy. We design a statist