Visual attention constitutes a very important feature of the human visual system (HVS). Every day when watching videos, images or browsing the Internet, people are confronted with more information than they are able to process, and analyze only part of the information in front of them. In parallel, crowdsourcing has become a particularly hot topic, enabling to scale subjective experiments to a large crowd with diversity in terms of nationalities, social background, age, etc. This paper describes a novel framework with the aim to bridge these two fields, by providing a new way of measurements of user's experience in a subjective crowdsourcing experiment. This study goes beyond self-reported methods, and provide a new kind of information for the context of crowdsourcing: visual attention. The results show that it is possible to estimate visual attention, in a non-intrusive manner and without using self-reported methods or specialized equipment, with a precision as high as 14.1% in the horizontal axis and 17.9% in the vertical axis. This accuracy is sufficient for many kinds of measurements that can be efficiently executed only in non-controlled environments..