  Facebook News Feed personalization algorithm has a significant impact, on a
daily basis, on the lifestyle, mood and opinion of millions of Internet users.
Nonetheless, the behavior of such algorithms usually lacks transparency,
motivating measurements, modeling and analysis in order to understand and
improve its properties. In this paper, we propose a reproducible methodology
encompassing measurements and an analytical model to capture the visibility of
publishers over a News Feed. First, measurements are used to parameterize and
to validate the expressive power of the proposed model. Then, we conduct a
what-if analysis to assess the visibility bias incurred by the users against a
baseline derived from the model. Our results indicate that a significant bias
exists and it is more prominent at the top position of the News Feed. In
addition, we found that the bias is non-negligible even for users that are
deliberately set as neutral with respect to their political views.
