Whether it be through a problematic related to information ranking (e.g. search engines) or content recommendation (on social networks for instance), algorithms are at the core of processes selecting which information is made visible. Those algorithmic choices have in turn a strong impact on users activity and therefore on their access to information. This raises the question of measuring the quality of the choices made by algorithms and their impact on the users. As a first step into that direction, this paper presents a framework to analyze the diversity of the information accessed by the users. By depicting the activity of the users as a tripartite graph mapping users to products and products to categories, we analyze how categories catch users attention and in particular how this attention is distributed. We then propose the (calibrated) herfindahl diversity score as a metric quantifying the extent to which this distribution is diverse and representative of the existing categories. In order to validate this approach, we study a dataset recording the activity of users on online music platforms. We show that our score enables to discriminate between very specific categories that capture dense and coherent subgroups of listeners, and more generic categories that are distributed on a wider range of users. Besides, we highlight the effect of the volume of listening on users attention and reveal a saturation effect above a certain threshold.