Here at the World Well-Being Project, we have done many crowdsourcing experiments over the past years. Often times, we are interested not only in what the workers annotate, but also in these workers themselves. For example, in a short paper  we have shown that females are better and more confident than males in guessing gender from tweets, especially when it comes to guessing females.
Similarly, many surveys are done over a non-random selection of participants. This is for example a problem in exit-polls, where a non-random population agrees to share their voting preference, leading to the need for pollsters to perform corrections a posteriori. Moreover, in online studies where users are anonymous, not all will agree to disclose self-identifying or personal information. Using data from our studies on the Amazon Mechanical Turk crowdsourcing platform, we aimed to uncover which users are more likely to voluntarily disclose their identity.